Quantum mechanics instruction production systems, methods, and applications thereof

ABSTRACT

Quantum Mechanics Instruction Production (QMIP) systems, methods, and computer-readable media are described. In some implementations, the QMIP system may comprise an input/output module, a database library module, a tradeoff module, a printer module, a samples analyzer module, and a test bench module. Some implementations can include a Nosanow Fermion and Boson Wave Module (or Nosanow Fermion Wave Module), a Grand Free Energy Module, an artificial intelligence module, a chemical bench module, a simulation module, and a metrology and interferometry module. The QMIP system can be programmed and configured to implement a quantum computer and algorithms that are executed on the quantum computer system, which notably, benefit from improved coherence and stability. In some implementations, the QMIP system may be programmed and configured to design new materials such as superconductors, superfluids, photovoltaics, and new drug therapies to treat disease.

RELATED APPLICATIONS

This application claims the benefit of U.S. Application No. 63/116,518,entitled “System and Method for Quantum Mechanics Instruction ProductionPlatform and Applications Thereof,” filed on Nov. 20, 2020, which isincorporated herein by reference in its entirety.

FIELD

Some implementations are generally related to quantum computing, and, inparticular, to a Quantum Mechanics Instruction Production (QMIP) systemthat can be programmed and configured to determine new materials forquantum computing, among other things, provide qubit set up information,and provide qubit coherence control instructions or control signals tohelp improve coherency duration.

BACKGROUND

Quantum mechanics is a fundamental theory in physics that provides adescription of the physical properties of nature at the scale of atomsand subatomic particles. It is the foundation of quantum physicsincluding quantum chemistry, quantum field theory, and quantumtechnology.

Classical physics, i.e., the description of physics that existed beforethe theory of relativity and quantum mechanics, describes many aspectsof nature at an ordinary (macroscopic) scale, while quantum mechanicsexplains the aspects of nature at small (atomic and subatomic) scales,for which classical mechanics may be insufficient. Most theories inclassical physics can be derived from quantum mechanics as anapproximation, which may be valid at a large (macroscopic) scale.

Quantum mechanics differs from classical physics in that energy,momentum, angular momentum, and other quantities of a bound system arerestricted to discrete values (quantization), objects havecharacteristics of both particles and waves (wave-particle duality), andthere are limits to how accurately the value of a physical quantity canbe predicted prior to its measurement, given a complete set of initialconditions (the so-called uncertainty principle).

Early quantum theory was profoundly re-conceived in the mid-1920s byNiels Bohr, Erwin Schrödinger, Werner Heisenberg, Max Born, and others.The original interpretation of quantum mechanics is the Copenhageninterpretation, developed by Niels Bohr and Werner Heisenberg during the1920s. The modern theory is formulated in various specially developedmathematical formalisms. In one of them, a mathematical function, thewave function, provides information about the probable amplitude ofenergy, momentum, and other physical properties of a particle.

In the early 20th century, it was found that subatomic particles andelectromagnetic waves are neither simply particle nor wave but havecertain properties of both particles and waves. This originated theconcept of wave-particle duality. Modern quantum physics provided auseful framework for many features of the modern periodic table ofelements and describes the behaviors of atoms during chemical bondingand the flow of electrons in computer semiconductors. And while quantummechanics was constructed to describe the world of the very small, it isalso needed to explain some macroscopic phenomena such assuperconductors and superfluids, which are of increasing importance asfoundational to numerous 21st century technologies.

“Quantum” refers to a discrete unit assigned to certain physicalquantities such as the energy of an atom at rest. The understanding thatparticles are discrete packets of energy with wave-like properties ledto the branch of physics dealing with atomic and subatomic systems,which is quantum mechanics. It underlies the mathematical framework ofmany fields of physics and chemistry, including solid-state physics,atomic physics, quantum chemistry, and so forth.

Quantum mechanics is essential for understanding the behavior of systemsat atomic length scales and smaller. In the formalism of quantummechanics, the state of a system at a given time is described by acomplex wave function, also referred to as state vector in a complexvector space. This abstract mathematical object allows for thecalculation of probabilities of outcomes of concrete experiments. Forinstance, electrons may be considered (to a certain probability) to belocated somewhere within a given region of space, but with their exactpositions unknown, as reflected by the well-known Heisenberg UncertaintyPrinciple.

The time evolution of a quantum state is described by the Schrödingerequation, in which the Hamiltonian (the operator corresponding to thetotal energy of the system) generates the time evolution. The timeevolution of wave functions is deterministic in the sense that—given awave function at an initial time—it makes a definite prediction of whatthe wave function will be at any later time. Analytic solutions of theSchrödinger equation are known for very few relatively simple modelHamiltonians including the quantum harmonic oscillator, the particle ina box, the dihydrogen cation, and the hydrogen atom. Even the heliumatom—which contains just two electrons—has apparently defied attempts ata fully analytic treatment. For the most part, only partial solutionshave been determinable for the Schrödinger equation.

Quantum mechanics is often the only theory that can reveal theindividual behaviors of the subatomic particles that make up most, ifnot all, forms of known matter (electrons, protons, neutrons, photons,and others).

In quantum mechanics, the Schrödinger equation is a linear partialdifferential equation that describes the wave function or state functionof a quantum-mechanical system. In classical mechanics, Newton's secondlaw (F=ma) is used to make a mathematical prediction as to what path agiven physical system will take over time following a set of knowninitial conditions. Solving this equation gives the position and themomentum of the physical system as a function of the external force onthe system. Those two parameters are sufficient to describe its state ateach time instant. In quantum mechanics, the analogue of Newton's law isSchrödinger's equation.

The Schrödinger equation predicts that if certain properties of a systemare measured, the result may be quantized meaning that only specificdiscrete values can occur. One example is energy quantization: theenergy of an electron in an atom is always one of the quantized energylevels. The challenge with the Schrödinger equation is that it is notdeterministic and has been solvable to describe the behavior andcharacteristics of relatively few scenarios, such as the hydrogen atom.In most other cases, the Schrödinger equation is used in atrial-and-error or ad hoc fashion to approximate the behavior ofphysical systems and materials.

A need may exist for methods and systems programming and configured toutilize quantum mechanics equations that provide a more accurateapproximation, or even an exact or nearly exact deterministicrepresentation, of physical systems and materials compared toconventional quantum mechanics equations. The present disclosure wasconceived in light of the above-mentioned needs, problems, orlimitations, among other things.

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

SUMMARY

Among other things, the disclosed subject matter involves thedevelopment of theories and equations which are solvable, and which canbe utilized to create or design a variety of systems, compositions orcompounds and other applications to solve technological problems.

Some implementations can include a Quantum Mechanics InstructionProduction System (QMIP) system. According to one or moreimplementations, the QMIP system comprises an input/output module, adatabase library module, a tradeoff module, a printer module, a samplesanalyzer module and a test bench module.

Some implementations have core processing modules including one or moreof a Nosanow Fermion and Boson Wave Module (or Nosanow Fermion WaveModule), a Grand Free Energy Module, an artificial intelligence module,a chemical bench module, a simulation module, and a metrology andinterferometry module.

According to one aspect, the QMIP system is applied to implement aquantum computer and algorithms that are executed on it, which notably,benefit from improved coherence and stability compared to conventionalquantum computing systems. According to yet another aspect, the QMIPsystem can be used to aid in the design of new materials such assuperconductors, superfluids, and photovoltaics. According to stillanother aspect, the QMIP system can be programmed and configured todesign new drugs to treat disease.

Some implementations can include a Quantum Mechanics InstructionProduction (QMIP) system comprising: a QMIP processing core includingone or more processors and one or more QMIP core process modules, aninput module coupled to the QMIP processing core, an output modulecoupled to the QMIP processing core, a database library module coupledto the QMIP processing core, a material printer coupled to the QMIPprocessing core, a sample analyzer coupled to the QMIP processing core,and a test bench coupled to the QMIP processing core.

In some implementations, the one or more processors can be coupled to acomputer-readable medium having stored thereon software instructionsthat, when executed by the one or more processors, cause the one or moreprocessors to perform operations. The operations can include obtaininginput requirements via the input module and searching the databaselibrary module for an existing material matching the input requirements.The operations can also include, when an existing material meets theinput requirements, outputting existing material information via theoutput module.

The operations can further include when an existing material does notmeet the input requirements, determining one or more new candidatematerials using the one or more QMIP core process modules by: performingfirst Grand Free Energy computations, computing Nosanow Fermion WaveEquation results, performing second Grand Free Energy computations,simulating the one or more new candidate materials based on the NosanowFermion Wave Equation, confirming that the one or more new candidatematerials based on the Nosanow Fermion Wave Equation meet the inputrequirements, and outputting information on confirmed ones of the one ormore new candidate materials determined by the QMIP processing core viathe output module.

In some implementations, the one or more QMIP core process modules caninclude: a Grand Free Energy module; and a Nosanow Fermion Wave module.In some implementations, wherein the first Grand Free Energy computationand the second Grand Free Energy computation are performed by the GrandFree Energy module, and wherein the Nosanow Fermion Wave Equationcomputations are performed by the Nosanow Fermion Wave module.

In some implementations, the one or more QMIP core process modulesfurther include: a simulation module, an artificial intelligence module,a chemical bench module; and a metrology and interferometry module. Insome implementations, the simulating of the one or more new candidatematerials can be performed by one or more of the simulation module andthe chemical bench module. In some implementations, the confirming ofthe one or more new candidate materials can be performed by one or moreof the chemical bench module, the metrology and interferometry module, atest bench module, and a sample analyzer module.

In some implementations, QMIP system can further comprise a license andauthorization security module, and a tradeoff module. In someimplementations, the input requirements include one or more of chemical,electrical, thermal, and electromagnetic properties.

In some implementations, when the one or more new candidate materialsincludes two or more candidate materials, performing a tradeoff analysisusing the tradeoff module and the artificial intelligence module. Insome implementations, the input requirements include specification of atransmon or Josephson junction.

In some implementations, performing the first Grand Free Energycomputations includes: calculating Grand Free Energy for a candidate newmaterial, determining a Grand Partition Function Trace based on theGrand free Energy calculation, computing Hamiltonians, and applying avariational theorem to determine an energy upper bound for the candidatenew material. In some implementations, computing the Nosanow FermionWave Equation results includes defining a Nosanow wave system,determining a commutator for free particles, determining a commutatorfor interaction particles, computing a time independent equation for theNosanow Fermion Wave Equation, and applying electromagnetic field to thetime independent equation for the Nosanow Fermion Wave Equation.

In some implementations, performing the second Grand Free Energycomputations includes: determining an Eigenvalue spectrum solution andsubstituting into the Grand Partition Function Trace, applying thevariational theorem to define solutions provided by the Nosanow FermionWave Equation, and defining phase transitions from solutions of NosanowFermion Wave functions to determine Tc.

In some implementations, the software instructions further includeinstructions that, when executed by the one or more processors, causethe one or more processors to perform further operations including:determining set-up parameters for configuring a quantum computer havingone or more qubits based on a selected one of the candidate newmaterials, providing the set-up parameters to a quantum computercontroller, determining one or more operational control parameters forthe quantum computer based on the selected one of the candidate newmaterials, and providing the operational control parameters to thequantum computer controller.

In some implementations, the quantum computer controller is a classicalcomputing device having one or more processors and the quantum computercontroller is integrated with the QMIP system.

In some implementations, the software instructions further includeinstructions that, when executed by the one or more processors, causethe one or more processors to perform further operations including:sensing one or more of temperature, pressure, and energy applied to theone or more qubits, and adjusting one or more of the temperature,pressure, and energy applied to the one or more qubits to maintaincoherency of the one or more qubits below Tc.

In some implementations, the one or more qubits includes a transmon. Insome implementations, the one or more qubits includes a Josephsonjunction.

In some implementations, the energy includes one of microwave energy orlaser energy.

In some implementations, the one or more qubits includes two or morequbits, and the software instructions further include instructions that,when executed by the one or more processors, cause the one or moreprocessors to perform further operations. The further operations caninclude generating Nosanow Fermion Wave Equation functions to couple thetwo or more qubits and generating control signals for the quantumcomputer controller to maintain coherency of the coupled two or morequbits.

In some implementations, the one or more qubits includemulti-dimensional Nosanow qubits, wherein dimensions of themulti-dimensional Nosanow qubits includes a spin dimension, and whereindetermining the one or more operational control parameters for thequantum computer is based on Nosanow Fermion Wave Equation functions andincludes utilizing the spin dimension of the multi-dimensional Nosanowqubits to generate control parameters and control signals from thequantum computer controller to maintain coherency of themulti-dimensional Nosanow qubits.

In some implementations, the spin dimension of the multi-dimensionalNosanow qubits is used to generate control signals to keep the selectedone of the candidate new materials in a superconductor zone and toextend coherency of the multi-dimensional Nosanow qubits.

Some implementations can include a Quantum Mechanics InstructionProduction (QMIP) system comprising: a QMIP processing core includingone or more processors and one or more QMIP core process modules, aninput module coupled to the QMIP processing core, an output modulecoupled to the QMIP processing core, a database library module coupledto the QMIP processing core, a material printer coupled to the QMIPprocessing core, a sample analyzer coupled to the QMIP processing core,and a test bench coupled to the QMIP processing core.

In some implementations, the one or more processors are coupled to acomputer-readable medium having stored thereon software instructionsthat, when executed by the one or more processors, cause the one or moreprocessors to perform operations. The operations can include obtaininginput requirements via the input module, searching the database librarymodule for an existing material matching the input requirements. Theoperations can also include when an existing material meets the inputrequirements, outputting existing material information via the outputmodule.

The operations can further include, when an existing material does notmeet the input requirements, determining one or more new candidatematerials using the one or more QMIP core process modules by performingfirst Grand Free Energy computations, computing Nosanow Fermion WaveEquation results, performing second Grand Free Energy computations,simulating the one or more new candidate materials based on the NosanowFermion Wave Equation, confirming that the one or more new candidatematerials based on the Nosanow Fermion Wave Equation meet the inputrequirements, and outputting information on confirmed ones of the one ormore new candidate materials determined by the QMIP processing core viathe output module.

In some implementations, the one or more QMIP core process modulesinclude a Grand Free Energy module and a Nosanow Fermion Wave module. Insome implementations, the first Grand Free Energy computation and thesecond Grand Free Energy computation can be performed by the Grand FreeEnergy module. In some implementations, wherein the Nosanow Fermion WaveEquation computations are performed by the Nosanow Fermion Wave module.

In some implementations, the one or more QMIP core process modulesfurther include a simulation module, an artificial intelligence module,a chemical bench module, and a metrology and interferometry module. Insome implementations, the simulating of the one or more new candidatematerials is performed by one or more of the simulation module and thechemical bench module. In some implementations, wherein the confirmingof the one or more new candidate materials is performed by one or moreof the chemical bench module, the metrology and interferometry module, atest bench module, and a sample analyzer module.

In some implementations, the software instructions further includeinstructions that, when executed by the one or more processors, causethe one or more processors to perform further operations. The furtheroperations can include determining set-up parameters for configuring aquantum computer having one or more qubits based on a selected one ofthe candidate new materials and providing the set-up parameters to aquantum computer controller.

The further operations can also include determining one or moreoperational control parameters for the quantum computer based on theselected one of the candidate new materials and providing theoperational control parameters to the quantum computer controller.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example QMIP system and associated environmentin accordance with one or more implementations described herein.

FIG. 2 is a diagram showing details of an example QMIP processing corein accordance with some implementations.

FIG. 3 is a diagram showing an example QMIP process in accordance withsome implementations.

FIG. 4 is a flowchart showing details of an example Grand Free Energyprocess in accordance with some implementations.

FIG. 5 is a flowchart showing details of an example Nosanow Fermion WaveEquation process in accordance with some implementations.

FIG. 6 is a flowchart showing details of an example Grand Free Energyprocess in accordance with some implementations.

FIG. 7 is a flowchart showing details of an example QMIP simulate andconfirm process in accordance with some implementations.

FIG. 8 is a diagram showing an example quantum computing control cyclein accordance with some implementations.

FIG. 9 is a diagram showing an example quantum computing stack inaccordance with some implementations.

FIG. 10 is a diagram showing a quantum computer including a Nosanowqubit and set up and coherency control components in accordance withsome implementations.

FIG. 11 is a diagram showing an example superconductor relationshipbetween temperature, current density, and magnetic field in accordancewith some implementations.

FIG. 12 is a diagram of an example QMIP system configured for industrialprocess control and optimization and an associated environment inaccordance with one or more implementations described herein.

FIG. 13 is a block diagram of an example quantum computing device havingone or more Nosanow qubits in accordance with some implementations.

FIG. 14 is a block diagram of an example classical computing devicewhich may be used for one or more implementations described herein.

FIG. 15 is a diagram showing an example virtual medical simulator inaccordance with some implementations.

FIG. 16 is a diagram showing an example Nosanow qubit set up andelectron tunneling in a Josephson junction in accordance with someimplementations.

FIG. 17 is a diagram showing an example Nosanow qubit and associateddrive and resonator components in accordance with some implementations.

FIG. 18 is a diagram showing an example Qubit Control using anelectromatic wave pulse defined by the Hamiltonian in accordance withsome implementations.

FIG. 19 is a diagram showing an example Nosanow qubit and associateddrive component in accordance with some implementations.

DETAILED DESCRIPTION Overview

Quantum mechanics is basically enshrined in three postulates: 1) thesuperposition principle, which governs the allowable possible states, 2)the measurement principle, which governs how much information about thestate can be accessed, and 3) unitary evolution, which governs how thestate of the quantum system evolves. Quantum mechanics describes thephysical properties of the order of atoms and subatomic particles, andis the foundation of quantum physics, quantum chemistry, quantum fieldtheory, quantum technology and quantum information science.

Most of classical physics can be described by quantum physics. Quantummechanics differs from classical physics in that energy, momentum,angular momentum, and other quantities of a bound system are restrictedto discrete values (quantization), objects have characteristics of bothparticles and waves (wave-particle duality), and there are limits to howaccurately the value of a physical quantity can be predicted prior toits measurement, given a complete set of initial conditions (theuncertainty principle). Quantum mechanics is modeled mathematically mostoften using Algebras and applying Hamiltonians. Erwin Schrodingerdefined a Wave Equation Hψ=Eψ for quantum mechanics that providesinformation about the probability amplitude of energy, momentum, andother physical properties of a particle.

The genesis of the disclosed subject matter herein takes into accountthe commonly assumed idea, which is that properties of Non-RelativisticQuantum Systems are thought to be determined by the Schrodinger WaveEquation Hψ=Eψ. It is generally believed this principle has been fullyestablished by experiment. This may not be the case and, among otherthings, it is this finding upon which our new theories of quantummechanics and the various implementations of the disclosed subjectmatter developed from their practical application to real worldtechnological problems are based upon. Prior to the disclosed subjectmatter, what has been enabled (e.g., technology applications, engineeredproducts, scientific solutions, etc.) by way of quantum mechanics hasbeen limited.

In general, a technical problem exists in that within the broadest rangeof science, organizations, and businesses there may be a need tounderstand and apply quantum mechanics to solve complicated challenges.Prior techniques have heavily relied on the Schrodinger Wave Equation,error correction algorithms, density function applications, simulationmodels, heuristics, and estimations. The prior art is such that eachentity has used a process that is either ad-hoc, based ontrial-and-error, or has introduced improvised adjustments. These priorsolutions have not produced the accuracy, reliability, or stabilitysought for applications that can address complex technological problemsand challenges that reflect social and economic needs.

Recent scientific, business and government sources indicate thattypically at least several years of work and research are requiredbefore quantum mechanics can be leveraged to provide a solution to agiven problem that achieves economic and national benefits. Problematicmaterial and tool design as well as their production, programdevelopment and computation procedures plague necessary advances.Fundamentally, the underpinning physics has not progressed quicklyenough to achieve essential breakthroughs. The following 11 areasexemplify this situation.

1) Quantum computing—there may exist a need to have longer lasting qubitcoherence (and, consequently, an ability to process highly complexprograms quickly) that is supported by enhanced quantum mechanicsunderstanding, engineering and materials design.

2) Qubit design and creation—e.g., initiating and tuning (e.g., thequbit frequency)-precisely can be difficult and involves adversetrade-offs and controlling qubits that leverage more than a singletwo-state quantum system (e.g., clusters of degrees of freedom), whichhas thus far appeared out of reach for conventional practicalapplications.

3) Quantum circuits and supporting hardware, superconductors,spintronics, advanced materials and fabrication improvements—such asdefining and producing new superconductors that reduce magnetic defectsand residual quasiparticles—may be lacking in some conventionalsolutions. Substantial requirements for temperature and isolationcontrol are critical and have been a limiting factor in makingadvancement in the quantum computing field. For example, semiconductoradvancements are often limited by trial and error.

4) Molecule, advanced material, chemical and drug design andproduction—the process of defining bond energies and reactionrequirements, structures, and the discovery and creation of newcompounds with advantaged qualities is subject to expensive andtime-consuming guesses and/or trial and error in some conventionalapproaches.

5) Metrology, interferometry and sensor applications—the existingtechnology to accurately measure matter and matter systemthermophysical, compositional and microstructural, as well aselectrochemical and electromagnetic properties may be limited.Conventional solutions often rely on numerous quantum noise dampeningstrategies that require various approximations, which is a significantdisadvantage.

6) Quantum computer programming language and algorithms—these aresubject to numerous limitations in conventional techniques. For example,conventional quantum computing languages and algorithms typically: (a)are not independent of circuit design; (b) require extensive code; (c)require significant error correction protocols; and (d) are notintuitive. Programming languages/algorithms are burdened by thechallenge to address uncomputation needs related to entanglement. Theexisting approaches may not readily enable new algorithms to be writtenfor problems that cannot be solved by classical computers alone.

7) Solutions to scientific problems involving quantum mechanics—existingquantum mechanical issues remain in numerous areas such as: general andquantum physics, cosmology, quantum gravity, high-energy and particlephysics, astronomy and astrophysics, nuclear physics,atomic-molecular-optical physics, classical mechanics, condensed matterphysics, plasma physics, and biophysics.

8) Instructions generation for direct production—an automatic compilerthat sends process steps for actual matter or matter system productreactors and/or information production may not be readily available.

9) Multidimensional Micro-Macro Production Printer—a system matter orproduct reactor that receives instructions to extrude products andinformation in various forms at a micro and macro multidimensional levelmay not exist in conventional systems. For example, there may be a needfor a system that would operate as an extension of an automated wetchemical bench and/or gas chromatography/mass spectrometry system toproduce actual product material and/or information results. Such aproduction reactor printer system may not exist in conventionalequipment.

10) Chemical Bench—an automated integrated wet chemical bench reactorand mass spectrometry system that is guided by a fully integratedArtificial Intelligence (AI) critical process system may not existwithin conventional systems.

11) Test Bench—an automated integrated multi-parameter test system forcompound or materials (e.g., pressure, torque, tension,electromagnetic); quantum computing (e.g., qubits, quantum chips,quantum dots); product (e.g., power conversion, fuel cell, battery,solar cell, etc.) that is guided by a fully integrated AI and criticalprocess system may not exist in conventional systems.

Some implementations can include a system and method to address theabove-mentioned technical problems or needs and that provides acomputerized multilateral system built upon a new quantum mechanicalcalculation process (e.g., the Nosanow Wave Equation described below)given specific property inputs that produce actual product materialand/or information. The system, referred to herein as the QuantumMechanics Instruction Production (QMIP) system determines materialcandidates using a materials data base library module (discussed furtherbelow). Using a computer processing system leveraging an AI Module,candidates are associated with the properties of the inputs. Based onthis association, the molecules, compounds, and materials are queued topredict their property qualities in the mechanical, thermal andelectromagnetic categories by way of the Nosanow Fermion and Boson WaveModule, discussed in detail later in this specification.

In some implementations, the Nosanow Fermion and Boson Wave Module isused to analyze each candidate and provide inputs to a Grand Free EnergyModule. A set of new and original Nosanow Wave Equations providesresults which are the inputs into the Grand Free Energy Module. TheGrand Free Energy Module then generates properties that are prioritizedfor best fit by leveraging the system's AI module. The original NosanowWave Equations, which constitute at least part of the new NosanowFermion and Boson Wave Module provide important information about theproperties of both Fermion and Boson particles and systems which cannotbe calculated (or would be less accurately calculated) using theSchrodinger Wave Equation. The series of equations in the NosanowFermion and Boson Wave Module are discernible and certifiable behaviorpredictors of quantum particles for Fermion and Boson systems. Theequations define and predict both known and unknown non-relativisticinformation about these new and previously unknown particles andassociated systems.

The QMIP system described herein provides a better understanding ofparticle matter and energy systems, their characteristic properties, howthey behave, exist, interact, and are constructively manipulatedcompared to conventional techniques. The QMIP system can include one ormore libraries of data about quantum physics with a new and novelunderstanding of how matter and energy actually work within theframework of the Nosanow Wave Equations.

More specifically, the QMIP system will take general requirements forproperly building quantum computing components, superconductors, chipsand computers, and other products and systems, and then will use newlygenerated insights into quantum mechanics phenomena (such as states,superposition, unitary time evolution, measurement, tunneling, andentanglement) in order to increase qubit coherence, which to date hasbeen a significant limiting factor in the viability and performance ofquantum computers and the use of quantum computing to create newproducts and systems and otherwise solve technological problems.

The QMIP system has the additional capability of providing better designinputs for a specific quantum chip and/or component based on newinformation for constructing junctions or gates that are derived in partfrom understanding superconductor discontinuity through the Nosanow WaveEquations.

The QMIP system can store chip or component characteristics, design, andfundamental materials properties—both macro and micro (e.g., mechanical,thermodynamic, electrochemical, electromagnetic)—in a library database.

The QMIP system can also perform extensive comparison analysis (e.g.,similarities, contrasts, counteractions, etc.) of the macro and microproperties (e.g., mechanical, thermodynamic, electrochemical,electromagnetic) of the chips, components, material, compounds, matterand other products and systems designed and created using the QMIPsystem and their performance conditions that establish increases inqubit coherence times. It is well known that qubit coherence times hasbeen a significant problem and limitation to existing quantum computersystems.

The QMIP system can provide tasks, conditions and standard instructionsfor how to produce the composition of matter materials.

In some implementations, the QMIP system incorporates an automatedchemical bench and 3D matter printer that will create the compositionand/or information of matter, qubits, chips and/or components forsampling and testing. Results of the QMIP system testing will be fedback into the QMIP system for analysis to evaluate trade-offs andproduction methods as well as to make suggestions about iterativeaspects as needed. An exemplary embodiment of the QMIP system is setforth in FIG. 1.

Advantages and Technical Solutions

Some implementations can include a quantum mechanics calculator that canprovide precise predictions of how quantum objects and systems behaveand may be controlled as well as how they drive macroscopic matterproperties and behavior. Some implementations can include enhancementsto DFT (Density-functional Theory), CALPHAD (CALculation of PHAseDiagrams) and VQE (Variational Quantum Eigensolver) based simulationsthat can produce unmatched accuracy advantages compared to conventionalsolutions.

A Quantum Mechanics Instruction Production system leverages more complexand efficient simulation algorithms that result in accurate materialpredictions. Referring to stage 2 of Table 1, a QMIP system describesparticle and system quantum mechanical properties more accurately aswell as predicts and produces new superfluids and superconductors thatcan operate at low and high temperature ranges. Also, the QMIP systemstores, generates, manages and delivers large amounts of data aboutmaterial properties and characteristics (in terms of both classical andquantum mechanical physics) that results in expanded repositories andnew quantum libraries related to material selection. Further, the QMIPsystem enables greater & faster computing power that will run complexalgorithms by efficiently leveraging quantum, and classical computerprinciples and capabilities. The QMIP system creates more efficientcomplex algorithms (applies a greater number of mathematical principlesand new solution processes that enhance existing models and simulationsas well as creates new ones that are employed to predict the optimalcompositions and structures based on select properties.).

Referring to stage 3 of Table 1, the QMIP system describes particle andsystem quantum mechanical properties more accurately, which can lead tothe discovery and design of new materials, such as superconductors thatcan operate on a range from low to high and beyond temperature limits;and that may be used in the material synthesis process. For example,high temperature can be anything above 20 degrees Kelvin and lowtemperature can be temperatures below 20 degrees Kelvin. Temperaturesbeyond can include temperatures above the ambient temperature of liquidnitrogen e.g., 77 degrees K. Also, the QMIP system stores, generates,manages and delivers large amounts of data about material properties andcharacteristics needed for successful fabrication (in terms of bothclassical and quantum mechanical physics) that result in expandedrepositories and new quantum libraries related to material fabrication.Further, the QMIP system provides greater and faster computing powerthat runs complex algorithms by efficiently leveraging operationalprocesses based on enhanced quantum, and classical computer principlesand capabilities. The QMIP system can creates more efficient complexalgorithms (applies a greater number of mathematical principles and newsolution processes that enhance existing models and product examinationas well as creates new ones that are employed as test simulations of thesynthesizing of compounds).

Referring to stage 4 of Table 1, the QMIP system describes: particle,atom and system quantum mechanical properties more accurately; that isused to design or identify new superconductors that operate on a rangefrom low to high and beyond temperature limits, which can be used forperformance testing. For example, high temperature can be anything above20 degrees Kelvin and low temperature can be temperatures below 20degrees Kelvin. Temperatures beyond can include temperatures above theambient temperature of liquid nitrogen e.g., 77 degrees K. Also, theQMIP system stores, generates, manages and delivers large amounts ofdata about synthesized material performance characteristics (in terms ofboth classical and quantum mechanical physics) that results insynthesized compound materials repositories and quantum librariesrelated to material performance tests. Further, the QMIP system providesgreater and faster computing power that runs complex algorithms byefficiently leveraging operational processes based on enhanced quantum,and classical computer principles and capabilities. The QMIP systemcreates more efficient complex algorithms (applies a greater number ofmathematical principles and new solution processes that enhance existingmodels and product examination processes as well as creates new onesthat are employed as test simulations of the actual synthesized materialcandidate's performance in a simulated actual point of use and undersimulated actual use conditions).

In reference to stage 5 of Table 1, the QMIP system describes particleand system quantum mechanical properties more accurately; designs andspecifies new superconductors that operate on a range from low to highand beyond temperature limits, and that can be used for industrialsynthesis (reactor vessel line construction and operationalrequirements—material, energy, pressure, temperature, electrical power,automation and time). For example, high temperature can be anythingabove 20 degrees Kelvin and low temperature can be temperatures below 20degrees Kelvin. Temperatures beyond can include temperatures above theambient temperature of liquid nitrogen e.g., 77 degrees K. The QMIPsystem stores, generates, manages and delivers large amounts of dataabout industrial synthesis processes (in terms of both classical andquantum mechanical physics) that result in synthesized compoundmaterials repositories and quantum libraries related to materialsynthesis on an industrial level.

A Quantum Mechanics Instruction Production system provides greater andfaster computing power that runs complex algorithms by efficientlyleveraging operational processes based on enhanced quantum, andclassical computer principles and capabilities.

The QMIP system defines, explains and predicts known non-relativisticparticle information about the systems; provides information forquasiparticle systems (non-relativistic); and gives a betterunderstanding of how matter and energy works, behaves, exists,interacts, and how its properties can be manipulated for industrialpurposes.

DESCRIPTION OF FIGS. 1 AND 2

As shown in FIG. 1, the QMIP system 100 includes a QMIP processing core102 having a classical computing device 104, a quantum computing device106, and QMIP core process modules 108. The classical computing device104 and/or the quantum computing device 106 can be integrated with theQMIP or can each be standalone devices.

The QMIP processing core 102 receives input 110 and produces output 112.The dashed line connecting the input 110 and output 112 indicates thatthe input 110 and output 112 sides can be separate modules or can beintegrated into one module including both input and output processingand data for the QMIP system 100.

In addition to the QMIP processing core 102, input 110 and output 112,the QMIP system 100 includes a license and authorization security module114, a database library module 116, a tradeoff module 118, a printer120, a sample analyzer 122, and a test bench 124.

In operation, the input/output modules (110, 112) generate and issueinstructions that control other modules of the QMIP system 100. Theseinstructions can be written in a format or code that is appropriate tothe particular target module so that no further compiling or translationof the instructions is required for the instructions to have theirintended control. Just as one example, the input/output modules (110,112) may issue instructions that control the wet chemical bench (e.g.,test bench 124) to automatically mix compounds and do so at specifiedpressures and temperatures. The input/output modules (110, 112) couldthen issue instructions to cause the printer module 120, which could beimplemented as a lithography machine, and its robotics to create aJosephson junction embedded in a wafer. The input/output modules (110,112) could then issue instructions to invoke the test bench module 124in order to test the properties and performance of a candidate product.For example, the test bench module 124 could be controlled so that itplaces the wafer in a holder and passes electrons through it in order totest voltage response. If the results of the product are satisfactory,the input/output modules (110, 112) could generate a set of instructionsthat could be given to and used by a third-party manufacturer of chipsto fabricate them using its selected lithography machine, such as onemade by ASML, Nikon, Canon, etc.

In some implementations, the QMIP system 100 provides an end-to-endsystem that enables the identification of requirements for a substanceor product, the automatic specification of the substance or product(e.g., how to make the substance or product and using what ingredients),the fabrication of the specified substance or product, and the testingof the specified substance or product to determine whether it providesthe desired properties or performance. The input/output modules (110,112) can include a processor for carrying out these operations and asuitable user interface.

In some implementations, the QMIP system 100 includes a number ofcomponents, which considered individually and/or as a combination, areunconventional and novel. The QMIP system 100 is a technologicalsolution to a technological problem and provides new functionalities andcapabilities to quantum computers and other computing technologies,described herein and in greater detail in the remainder of thisspecification. The QMIP system 100 itself provides new functionalitiesand capabilities that previous solutions involving conventional theoriesof quantum mechanics do not provide. These capabilities and functionsare provide, in part by the core processes of the QMIP system.

FIG. 2 is a diagram showing details of example QMIP core processingmodules 108 in accordance with some implementations. The core processingmodules 108 include a Nosanow Fermion and Boson Wave Module 202, a GrandFree Energy Module 204, a simulation module 206, an artificialintelligence (or machine learning) module 208, a chemical bench module,a metrology and interferometry module 212.

Some implementations can include artificial intelligence (AI)functionality (e.g., provided by the AI module 208) including algorithmsthat parse newly discovered data from the modules (in particular theNosanow Fermion and Boson Wave Module 202), learn from the data, andthen apply the resulting learned information to make decisions (e.g.,generate instructions) for the entire system to perform.

Unique machine interface instructions that automatically guide thechemical bench module 210, test bench 124, sample analyzer 122 andprinter modules 120 through coherent requirements.

An automated full wet bench operation with integrated automated gasanalytic chromatography and mass spectrometry that combines features ofeach system to identify substances within a test sample (e.g., 214).

In some implementations, the QMIP system 100 includes amulti-dimensional matter printer (e.g., 120) capable of extrudingmicro-macro compounds and composites.

QMIP Material Design

Table 1 illustrates an exemplary general material design workflowaccording to one embodiment of the disclosed subject matter. Asillustrated in Table 1, the second row (challenges) reflectsdeficiencies and drawbacks of known approaches to material design ascompared to the third row (advantages), which reflects at least some ofthe benefits and improvements brought by the disclosed subject matter:

The QMIP system 100 and associated methods described herein providenumerous benefits and applications. For example, the QMIP system 100describes particle and system quantum mechanical properties moreaccurately, discovers new superconductors which operate at a range fromlow to high and beyond temperature limits and that can be used to definenew and existing material properties. For example, high temperature canbe anything above 20 degrees Kelvin and low temperature can betemperatures below 20 degrees Kelvin. Temperatures beyond can includetemperatures above the ambient temperature of liquid nitrogen e.g., 77degrees K.

Some implementations can include a QMIP system that stores, generates,manages and delivers data about material properties and characteristics(in terms of both classical and quantum mechanical physics) resulting inrepositories and libraries of material micro and macro properties andtheir use. For example, material properties and characteristics data canbe stored in the database library module 116. In some implementations,the QMIP system 100 can provide computing techniques to run complexalgorithms to be efficiently leveraging innovative quantum, andclassical computer principles and capabilities.

In some implementations, the QMIP system 100 can provide more efficientcomplex algorithms and apply a greater number of mathematical principlesand new solution processes that enhance existing models and industrialreactor material synthesis procedures and processes that are optimizedfor material adoption and use.

For example, with conventional techniques, an engineer designing a newwing box, a superconductor for a special environment, a photovoltaicpanel, a battery, or a windmill blade would, as her first step, retrieveelectronic version of dozens or more volumes of material design. She mayhave the previous successful projects as examples to start. The choicesmay be many, and determining the “best” material for a problem maydepend on requirements, costs, practicability, time, toxicity,stability, starting conditions, steady state conditions, overloadconditions, lift, drag, magnetic properties, etc. From previoussuccesses, she could try different variations to get better performance.The conventional process often involves research with an incompleteunderstanding of how matter and energy works, trials of new combinationsof material, and lots of errors as well as rejections.

One technical problem in new material development includes choosing thebest material for a project involves what worked before, disparate hardand soft copy volumes of material science based on incomplete data andapproximations of how matter and energy actually work in situ. Theengineer choosing the “best” material is limited by imperfect underlyingmatter and energy theory, disparate libraries of knowledge, andcandidate materials trial and error results.

In some implementations, the QMIP system 100 can explain and predictbehavior of quantum particles through the mathematically proven NosanowWave Equations for Fermion and Boson particles and systems. The QMIPsystem 100 can translate behaviors from the quantum micro-level to themacro-level in the categories of electromagnetic and chemicalproperties, just to name a few.

The QMIP system according to an embodiment of the present disclosureincorporates one or more database libraries (e.g., 116) of materialscience with a new and novel understanding of how matter and energyactually work. The QMIP system 100 takes the requirements of a project,prepares initial solutions, tradeoffs, and definitions of best criteria.The system can then provide a feasible solution for composites, alloysor compounds beginning with energy interactions leading to molecularstructures that address the requirements, tradeoffs, and/or optimizationcriteria.

The QMIP system 100 has the additional capability of leading to bettermatter and energy description and characteristics of specific chemicalcompounds, drugs, isotopes, elements, ions, nuclides and/or compositionsof matter.

QMIP stores characteristics of the compound or matter (such as free andinteractive energies) in the one or more libraries (e.g., 116). QMIP cancompare and contrast compounds matter and energy functions and theircharacteristics. QMIP can start with a set of initial solutions and findsolutions that fit the requirements, tradeoffs and best criteria ascompared to the original solutions. QMIP can also find solutions thatare not as “good” as the initial solutions, but still satisfy therequirements.

QMIP incorporates a wet chemical bench system 210 and a 3-D matterprinter 120 that produce the candidates for sample testing (e.g., using122) and form fit testing. Results from testing will be fed back throughthe QMIP system 100 to evaluate implications and make candidateimprovement suggestions as needed.

QMIP artificial intelligence 208 can guide an engineer to discover theright tradeoffs for her problem. QMIP will be able to test the candidatesamples (e.g., using test bench 124) and provide feedback to theArtificial Intelligence to enable advancements in process improvementprograming.

The QMIP system can predict chemical properties and creates newcompounds; predict compounds to meet requirements; and predict chemicalproperties of new compounds. QMIP analyzes a sample and predicts newcompounds with similar or enhanced chemical properties that require lessmatter and create less byproducts. QMIP predicts new superconductors towork in specific temperature and pressure ranges.

QMIP predicts and creates new superconductors based on the instructionsfor production.

How does an engineer (quantum investigator) create a new compound, basedon their requirements, using the Quantum Mechanics InstructionProduction (QMIP) system and platform?

First, the engineer needs to choose a problem from the possibilitiesthat the equations can address. The following partial list sets forthsome non-limiting examples:

-   -   Superconductor theory and new superconductors,    -   Superfluid theory and new superfluids,    -   Quantum computers and Quantum Computer components,    -   Creating qubits having more than two states and decreasing the        decoherence in the Quantum Computer system,    -   Improved Josephson Junctions used in quantum computers,    -   Solid state electronics and improved semiconductors, to include:        -   Photovoltaic,        -   Thermo Generator,        -   Light Emitting Diode (LED)        -   Organic light Emitting Diode (OLED)        -   Semiconductor lasers,        -   Charged Coupled Device (CCD)        -   Electric storage devices or improved batteries    -   Chemical agents such as catalyst, inhibitors and emulsifiers        -   A catalyst's improvement of the Haber-Bosch process for            fertilizer production such as understanding the quantum            mechanics involved and optimizing the tradeoffs among yield,            by-products and energy used,        -   Improve understanding of Low Energy Nuclear Reaction (LENR)            reactions,        -   Other Chemical reactions and chemical insights,    -   Improved nuclear shell model theory,    -   Ability to trace the quantum particle theory through the        electromagnetic process, to the macro world of a systems        thermodynamic properties,    -   Nuclear Reactions to include fusion and plasma processes,    -   Understanding the existence of isotopes,    -   Heat Generation such as Low Energy Nuclear Reaction (LENR),    -   Advanced Quantum Simulations,    -   Better material designs,    -   Improvements in the periodic table based on the Nosanow Wave        equations,    -   Creation of a chemical bench to make the new substances,    -   Creation of a 3D matter and energy printer to create the correct        form factor for testing the substance in the practical        application of the component, circuit, system, quantum computer,        or medical application,    -   Ability to test the newly created compounds to ensure the        chemical bench correctly built the compound and that the        compound works as predicted,    -   Ability to test that the new form factor or circuit works, and    -   How matter and energy work and how to isolate and make special,        desired properties appear.

As examples, the engineer needs to determine why a superconductor iselectron frictionless or why sunlight is turned into electricity. With astarting solution, what works now or before, the QMIP uses the startingsolutions' known solution (Xx_(n)Yy_(m)) and uses the artificialintelligence module 208, Nosanow Fermion Wave Module 202, and Grand FreeEnergy Module 204 to obtain the Grand Free Energy. The Grand Free Energyis key to determining the bonds, frequency, chemical properties, andthermodynamic variations that make the initial solution unique.

DESCRIPTION OF FIGS. 3-7

FIG. 3 shows a flowchart for an example process 300 of discoveringand/or creating a new material such as a superconductor or superfluid,or other product using the QMIP system (e.g., 100) in accordance withsome implementations. Processing begins at 302, where a first portion ofa Grand Free Energy equation is calculated. The Grand Free Energy can becalculated using the Grand Free Energy module 204. Processing continuesto 304.

At 304, Nosanow Fermion Wave Equation functions are computed using, forexample, the Nosanow Fermion and Boson Wave Module 202. Processingcontinues to 306.

At 306, a second portion of the Grand Free Energy is calculated using,for example, the Grand Free Energy module 204. Processing continues to308.

At 308, one or more candidate new materials is simulated and confirmed.

The details of each of steps 302-308 are described below in conjunctionwith FIGS. 4-7, respectively. This is example shows how animplementation of the disclosed subject matter can be used findsuperconductors or superfluids by identifying phase discontinuitiesusing modules such as the simulation module and the AI module describedherein.

FIG. 4 is a diagram showing details of the first Grand Free Energycalculations 302 in accordance with some implementations. Processingbegins at 402, where the QMIP processing begins with the Grand Ensemblefrom which one determines the Grand Free Energy that may be necessary.The aim is to determine where T_(c) must exist.

$\begin{matrix}{{{{Grand}\mspace{14mu}{Free}\mspace{14mu}{Energy}\mspace{14mu}{\Gamma( {T,V,\mu} )}} \equiv {{- T}\mspace{14mu}\ln\mspace{14mu} Z\mspace{14mu}( {T,V,\mu} )}};} & (1)\end{matrix}$

the Grand Ensemble is used to calc the Grand Free Energy, processingcontinues to 404.

At 404, the Partition Function is used to calculate the Grand FreeEnergy in which a Hamiltonian and wave functions are to be recognized.Here temperature (T) is represented as energy.

$\begin{matrix}{{{{Grand}\mspace{14mu}{Partition}\mspace{14mu}{Function}\mspace{14mu}{Z( {T,V,\mu} )}} \equiv {{Tr}\lbrack e^{{- \beta}\;{H{(\mu)}}} \rbrack}},{{\beta \equiv \frac{1}{T}};{{temperature}\mspace{14mu}{is}\mspace{14mu}{written}\mspace{14mu}{in}\mspace{14mu}{energy}\mspace{14mu}{units}}}} & (2)\end{matrix}$

Processing Continues to 406.

At 406, the general Hamiltonian (note: the classical Hamilton is definedby potential and kinetical energy) can now be worked by the NosanowHamiltonian, Ĥ_(CF). One now can describe the system more accurately anduse this Hamiltonian in ways to solve for the Wave functions. One cannow use the Variational Theorem for establishing an approximation.

$\begin{matrix}{{{{Hamiltonians}\mspace{14mu}{\hat{H}(\mu)}} \equiv {{\hat{H}}_{CF} - {\mu\;\hat{N}}}};{{\hat{H}}_{CF} \cong {\lbrack {{\hat{H}}_{F} + {\hat{H}}_{GP}} \rbrack + \lbrack {\hat{H}}_{a} \rbrack}}} & (3)\end{matrix}$

Ĥ_(CF) is a Nosanow Hamiltonian designated here as the Cooper FermionHamiltonian. This is turn refines the BCS theory making it morecomplete, accurate, comprehensive and expansive.

μ is the chemical potential.

{circumflex over (N)} is number of particles.

Ĥ_(F) is the free particle Hamiltonian.

Ĥ_(GP) is the Grand Pairing Hamiltonian.

Ĥ_(α) is the Interaction Hamiltonian. Processing continues to 408.

At 408, the Variational Theorem is used to get the best fit estimate ofH_(F) and H_(GP). Note: the parabola representation is just an exampleto illustrate a starting point to establish the energy upper bound.

Variation Theorem Hamiltonian)v=v₀+(½)(x−x₀)²; one ex. used to determineenergy upper bound  (4)

Referring to FIG. 6, the engineer will input the problem in theInput/output modules (110, 112). The investigator needs to enter therequirements, starting solutions, library initial conditions, desiredchemical properties, and tradeoff properties. Good requirements arehelpful for any problem-solving exercise. Incorrect requirements canlead to solving the wrong problem. The requirements should be feasible,verifiable, traceable, understandable and complete. It is envisionedthat the requirements component drives the investigation. The engineermay have several starting solutions that are known to have some of thedesired characteristics, such as a superconductor that works at a highertemperature or a photovoltaic material that works with XX % conversionefficiency. The database library 116 are consulted to check foradditional starting solutions or feasible solutions that may alreadyexist. In a later exemplary case, the engineer can take a sample of theoriginal material and use the analysis of the original sample(s) to bethe starting solution. The desired output's chemical properties couldinclude the ability for electrons to flow without friction, solar andheat thermals to cause the flow of electrons, ability to give off heat,ability to bind or repel, increase or decrease magnetism, ability to bea catalyst and create a chemical reaction with minimal byproducts, etc.The above requirements are akin to functional requirements, which arethe properties the user desires. Non-functional requirements are thetechnical requirements dealing with performance issues. Thesenon-functional requirements are often tradeoff properties, such as cost,manufacturability, stability, toxicity, and other special properties.Creating a room-temperature superconductor that can be unstable atnormal atmospheric pressure but toxic might be of little value. The samewould be the case for a photovoltaic material with a high sun light toelectricity conversion rate but at a high cost.

The input module 110 is an intensive and initially time-consuming moduleinto which the correct context of the request must be established. Oncedone, the input module 110 will save a tremendous amount of effort andhelp focus the investigator's search.

According to an embodiment, and referring to FIG. 1, the databaselibrary 116 will contain data from the modified periodic table(recalculated based on the QMIP's newly acquired knowledge and insights)and will progress as the QMIP assists the investigators with moresearches and the creation of new materials, products and so forth. Theknowledge of material science will be entered into the database library116 and in some cases corrected or updated or flagged where conflictsexist. In some cases, an engineer's search will start and end with thedatabase library 116 as the solution may already exist as a result ofprevious searches or QMIP process runs. The Artificial intelligencemodule 208 (FIG. 1) will data mine the database library module 116 todetermine what answers and/or near answers may exist.

Access to the database library module 116 (and to the QMIP system ingeneral) can be controlled by a license and authorization securitymodule 114. This security module 114 can help prevent unauthorizedusers, searches, retrieval of information and input into the databaselibrary 116 without previous authorization, permissions, or license. Itis foreseen that an engineer's search and discoveries may be keptsecret/confidential within the QMIP system 100 and the database library116 and kept confidential for an appropriate period of time. In oneembodiment, restrictive warnings could be issued due to the creation oridentification of a material or product having a high toxicity or lowstability that in turn could trigger required special oversight.Preferably, prevention of false data being entered into the databaselibrary 116 would be flagged and denied storage. A specific company'sresearch and development work would be kept on secure nodes orKubernetes to prevent competitors from benefiting from the company'sefforts. The license and authorization security module 114 could alsohave company/organization/country specific restrictions for specificsubjects such as nuclear weapons research or other research involvingnuclear reactions.

In some implementations, the QMIP process could begin with an initialsolution and leverage the Nosanow Wave Equations in combination withprocedures such as that set forth herein.

FIG. 5 is a flowchart showing details of an example Nosanow Fermion Waveequation process 304 in accordance with some implementations. Processingbegins at 502, where the wave function is defined with the HeisenbergEquation of Motion, a wave description of the particle. For example:

$\begin{matrix}{{{{Define}\mspace{14mu}{the}\mspace{14mu}{wave}\mspace{14mu}{system}\mspace{14mu} i\;\hslash{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )}} = {\lbrack {{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )},{\hat{H}}_{CF}} \rbrack \equiv \lbrack {{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )},{{\pm {\hat{H}}_{Free}} + {\hat{H}}_{\overset{\_}{a}}}} \rbrack}}{{{Heisenberg}\mspace{14mu}{{Eq}.\mspace{14mu}{of}}\mspace{14mu}{Motion}},{{used}\mspace{14mu}{to}\mspace{14mu}{eventually}\mspace{14mu}{calc}\mspace{14mu}{the}\mspace{14mu}{{Tr}({Trace})}}}} & (5)\end{matrix}$

QMIP applies the quantization rules to Fermions and Bosons (inrelationship to the process of the canonical field quantization).Processing continues to 504.

At 504 and 506, the Wave Equation is solved for using commutators forfree particles and commutators for the interaction particles.

$\begin{matrix}{{{{Commutator}\mspace{14mu}{for}\mspace{14mu}{the}\mspace{14mu}{free}\mspace{14mu}{{particles}\mspace{14mu}\lbrack {{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )},{\pm {\hat{H}}_{Free}}} \rbrack}} = {{\pm ( {\overset{arrow}{r},\overset{arrow}{S}} )}{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )}}};{{Quantization}\mspace{14mu}{rules}\mspace{14mu}{for}\mspace{14mu}{Fermi}\mspace{14mu}{particles}}} & (6)\end{matrix}$

Processing Continues to 508.

At 508, the QMIP system combines the free and interaction Hamiltoniansto create the new Nosanow Fermion Wave Equation.

$\begin{matrix}{{{The}\mspace{14mu}{Time}\mspace{14mu}{IndependentEquation}\mspace{14mu}{for}\mspace{14mu}{the}\mspace{14mu}{NosanowFermion}\mspace{14mu}{Wave}\mspace{14mu}{Equation}\text{:}\mspace{14mu}\{ {{\pm {D( {\overset{arrow}{r},\overset{arrow}{S}} )}} + {\frac{1}{V}{\sum\limits_{\eta_{1}}{\sum\limits_{\eta_{2}}{\sum\limits_{\eta_{4}}{\int\limits_{V}{d{\overset{arrow}{r}}_{2}\langle {\eta_{1},{\eta_{2}{{V_{\overset{\_}{a}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},\eta_{3}} \rangle \times {\psi^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}{\psi( {{\overset{arrow}{r}}_{2},\eta_{2}} )}}}}}}}} \}{\psi( {\overset{arrow}{r},\eta} )}} = 0} & (8)\end{matrix}$

Processing Continues to 510.

At 510, the QMIP generalizes the Nosanow Fermion Wave Equation to theElectromagnetic Field.

$\begin{matrix}{{{Apply}\mspace{14mu}{Electromagnetic}\mspace{14mu}{Field}\mspace{14mu}{to}\mspace{14mu}{the}\mspace{14mu}{Time}\mspace{14mu}{IndependentEquation}\mspace{14mu}{for}\mspace{14mu}{the}\mspace{14mu}{NosanowFermion}\mspace{14mu}{Wave}\mspace{14mu}{Equation}\text{:}\mspace{14mu}\{ {{\pm {D( {\overset{arrow}{r},\overset{arrow}{S}} )}} + {\frac{1}{V}\Sigma_{\eta_{1}}\Sigma_{\eta_{2}}\Sigma_{\eta_{4}}{\int_{V}{d{\overset{arrow}{r}}_{2}\langle {\eta_{1},{\eta_{2}{{V_{\overset{\_}{a}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},\eta_{3}} \rangle \times {\psi^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}{\psi( {{\overset{arrow}{r}}_{2},\eta_{2}} )}}}}} \}{\psi( {\overset{arrow}{r},\eta} )}} = 0} & (9)\end{matrix}$

FIG. 6 is a flowchart showing details of an example second portion of aGrand Free Energy process 306 in accordance with some implementations.

One actually solves for the wave eigenfunctions and eigenvalues.Processing begins at 602, where the QMIP system determines the waveeigenfunctions and eigenvalues

$\begin{matrix}{{{{Commutator}\mspace{14mu}{for}\mspace{14mu}{the}\mspace{14mu}{interaction}\mspace{14mu}{{particles}\mspace{14mu}\lbrack {{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )},{\hat{H}}_{\overset{\_}{a}}} \rbrack}} = {\frac{1}{V}{\sum\limits_{\eta_{1}}{\sum\limits_{\eta_{2}}{\sum\limits_{\eta_{4}}{\int\limits_{V}{d{\overset{arrow}{r}}_{2}\langle {\eta_{1},{\eta_{2}{{V_{\overset{\_}{a}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},\eta_{3}} \rangle \times {\psi^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}{\psi( {{\overset{arrow}{r}}_{2},\eta_{4}} )}{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )}}}}}}}}\mspace{76mu}{{Quantization}\mspace{14mu}{rules}\mspace{14mu}{for}\mspace{14mu}{Fermi}\mspace{14mu}{particles}}} & (7) \\{{{Eigenvale}\mspace{14mu}{Spectrum}\mspace{14mu}( {{soln}.} )\mspace{14mu}{of}\mspace{14mu} E\;{\psi( {\overset{arrow}{r},\eta} )}};{{substitute}\mspace{14mu}{into}\mspace{14mu}{Grand}\mspace{14mu}{Partition}\mspace{14mu}{Function}\mspace{14mu}{Trace}}} & (10)\end{matrix}$

Processing Continues to 604.

At 604, the QMIP system uses the Variational Theorem again to helpdefine solutions of the wave functions, which in turn helps define theproperties of potential superconductors.

$\begin{matrix}{{Variational}\mspace{14mu}{Theorem}\mspace{14mu}{helps}\mspace{14mu}{define}\mspace{14mu}{\psi( {\overset{arrow}{r},\eta} )}\mspace{14mu}{and}\mspace{14mu}{the}\mspace{14mu}{meaning}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{solutions}\mspace{14mu}{given}\mspace{14mu}{by}\mspace{14mu}{the}\mspace{14mu}{wave}\mspace{14mu}{functions}} & (11)\end{matrix}$

Processing Continues to 606.

At 606, QMIP determines phase transitions such as the criticaltemperature for the potential superconductor and/or superfluid.

$\begin{matrix}{{Define}\mspace{14mu}{the}\mspace{14mu}{Phase}\mspace{14mu}{Transition}\mspace{14mu}{from}\mspace{14mu}{the}\mspace{14mu}{solutions}\mspace{14mu}( {T,V,\mu} )\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{wave}\mspace{14mu}{functions}\mspace{14mu}{to}\mspace{14mu}{determine}\mspace{14mu} T_{c}} & (12)\end{matrix}$

An important insight is that the Nosanow Wave Equation greatly improvesthe Density Function Theories with a more exact solvable wave equationas well as improves the electron cloud estimate.

Improved Density Function Theories (DFT) will simulate the chemicalbonds, compounds, and chemical properties (i.e., superfluids and/orsuperconductors). CALPHAD stands for CALculation of PHAse Diagrams. TheNosanow Fermion Wave Module 202 will actually address phasediscontinuities which CALPHAD cannot do today. VQE stands forVariational Quantum Eigensolver which will be improved with a betteransatz state provided by the Nosanow Fermion Wave Module 202 to find theground state the molecules, as shown below in conjunction with FIG. 7.

FIG. 7 is a flowchart showing details of an example QMIP simulate andconfirm process 308 in accordance with some implementations.

Processing begins at 702, where a simulation is performed using improvedCalphad, DFT, and VQE techniques as discussed above. (13) Processingcontinues to 704.

At 704, the engineer or designer conducts experiments to confirm thetradeoff requirements are satisfactory for the specific application. Forexample, the qualities of properties are confirmed given measurementstandards of the requirements. (14) Processing continues to 706.

At 706, the Artificial Intelligence module 208 establishes or recognizesthe necessary QMIP process and then helps evaluate from the millions andbillions of candidates for the requirements best fit. This process couldbe even further enhanced by Quantum Computers. For example, applyartificial intelligence to identify optimum candidates, redirectsimulation, establish production instructions, and perform libraryinput/output. (15)

The engineer uses the Database Library of chemical potentials and bondsto find the correct and ideal combinations of matter. Given that theQMIP has selected candidates for the requirement properties, one can nowcomplete the tradeoffs of a superconductor, for a malleable metal to usein a Tokamak, for example. A Tokamak (Russian: ToκaMáκ) is a devicewhich uses a powerful magnetic field to confine a hot plasma in theshape of a torus. The Tokamak is one of several types of magneticconfinement devices being developed to produce controlled thermonuclearfusion power.

Throughout the process, the artificial intelligence module 208 guidesthe selection of candidates and how the candidates are balanced withtradeoffs and the requirements using, for example, trained model asdiscussed below. The AI Module 208 helps guide all Benchwork via theinstructions for production. For example, the AI Module 208, given theNosanow Fermion Wave Module's 202 results, can provide the process stepsfor how the compound is to be created. In some implementations, the AImodule 208 can be configured to model the compound (or system) using theNosanow wave equations, which describe how the system behaves. (e.g.,bonds, valence cloud, etc.). The AI module 208 can inform which compoundis “best” based on the modeled candidate compounds and their respectiveproperties. The tasks, conditions and standards are provided to theautomated benches and the printer extruder for the wire shielding of theTokamak. These steps can be repeated as the process may have severalestimates that demand refinement.

Depending upon circumstances many variations of the step process brieflyoutlined above can occur. The numbering is not meant to be a strictsequence that must be followed but merely a symbolic outlining of ageneral process.

Most of the steps outlined above are broken down between the NosanowFermion Wave module 202 and the Grand Free Energy 204 Modules. The GrandFree Energy Module 204 solves for quantities such as the thermodynamicproperties, phase transitions, and electromagnetic characteristics.

The Nosanow Fermion and Boson Wave Module 202 solves the wave equation(eigenvalues and eigenfunctions) in terms of energy. According to oneembodiment, the Nosanow Fermion Wave Module is designed around severalcore steps shown in FIG. 5 and discussed above.

With reference to FIG. 1, the QMIP takes the Inputs (110, 112), andprovides the inputs to the artificial intelligence module 208 to searchthe database library 116, check the license and authorization securityaccess module 114, and uses the QMIP Process Modules shown in FIGS. 1and 2 to implement the steps that create the outputs. Preferably, theQMIP Outputs Module (110, 112) presents the candidates' composition ofmatter, for example, which have been derived from applications of uniqueHamiltonians, Grand Free Energy valuations, predicted chemicalproperties, structural molecular formulations to include the chemicalbonds in a 2D or 3D structure, stages of reaction and byproducts,frequency controls and sensitivities as well as particle interactionsand their thermodynamic properties. Just as important, candidates havespecific instructions for their composition and production. Thesedetailed instructions of production are based on the insights from theAI 208 and the Nosanow Fermion Wave Equation 202 Modules and inform theengineer, who may be part theoretical physicist and part chemist, howthe chemical bench module 210 and the envisioned 3D matter and energyprinter module 120 are to construct the composition.

The QMIP uses Artificial Intelligence 208 and the Simulation module 206Modules to assist evaluating the potentially millions of differentcombinations to come up with likely candidates to meet the requirements.Artificial Intelligence (AI), also called machine learning, is computerintelligence to solve problems through intensive and repetitive learningof the perceived environment to make decisions to optimize the statedgoals. The Artificial Intelligence learns from the potentially millionsand even billions of possibilities to help the engineer to narrow theset of possibilities to a more manageable set.

Form and fit can have a large influence upon design and on finding newmaterials. A problem in finding new matter or material for compositesthat fit the requirements may be due to exceptional needs. For instance,a new photovoltaic material might have to fit within a certain solarcell shape that will be on a satellite and have specific weight anddurability requirements. Another example is a new superconductor wouldneed to fit within a specific Tokomak for a plasma generator. Anothersuperconductor could have different requirements for use by a JosephsonJunction used within Quantum Computers. In these examples, a computeraided design (CAD) blueprint or the equivalent exists, and the newmaterials will enhance performance of the current circuit design whichit describes.

The CAD blueprint is part of the requirements and should be taken intoaccount in the chemical bench 210 and the 3D printer module 120 Modules.The CAD blueprint (or equivalent) is of a circuit, chip, component,wire, junction, connector, plate, sub-system or system of which that thenew material must be compatible. Wanting a solid wire and getting a gelis an example of a mismatch with CAD design.

The candidate composition of matter (and energy), (Xx_(a)Yy_(b)), arescored on how well the Candidates meet the Requirements as measuredwithin the Tradeoff module 118. The QMIP may have one Candidate ormillions of Candidates for the engineer to examine. The RequirementsScorer and the Tradeoff Scaler allow the engineer to do tradeoffs and“what ifs” with the candidates. For example, the engineer might want tohave a superconductor that can perform across a large temperature range,and the QMIP has identified several candidates that work in the lowerrange and several candidates that will work in the upper range. So,having multiple candidates that fulfill the requirement might beacceptable. The Tradeoff Scaler allows the weighing of the Candidatesfrom a performance viewpoint (non-functional requirements) as the easeof manufacturing and cost might be a tie breaker for several candidates.The results of the Tradeoffs and Candidates are fed back to the databaselibrary 116 to increase the body of knowledge. Each Candidate hasdetailed Instructions of Production for its composition. The NosanowFermion Wave Module 202 coupled with the AI Module's insight of howbonds, matter and energy work as an integrated whole makes theseInstructions for Production possible and unique. It is envisioned thatthe Instruction for Production also include the molecules' structuralformulation that incorporates the chemical bonds. For the bonds, theinstructions include the types of bonds needed, such as ionic bonds,covalent bonds, polar bonds and hydrogen bonds, etc.

The tradeoff module 118 interacts with the chemical bench module 210,simulation module 206 and matter and energy printer 120 modules. Thevirtual simulation module 206 runs the candidates' Instruction forProduction through a variety of sampling and tests to verify QMIPcandidate results. Known problems or discontinuities are handled and theresults are fed back into the tradeoff 118 and database library 116Modules. The tradeoff module 118 could then also initiate a cyclethrough the chemical bench module 210 and the Printer Module 120resulting in new candidates to test. This is assisted by the artificialintelligence module 208 that learns how to improve the entire system.

The chemical bench 210 and printer module 120 Modules are envisioned asan Engineer's advanced physics and chemist workbench. QMIP thus enablesengineers' fuller understanding of how matter and energy work, predictnew compounds and based on the knowledge and predictions can now be ableto build, create, manufacture, or synthesize new composition of matterknowing their respective kinetic and potential energy characteristics.These issues are captured in the Instructions for Production. Theengineer is able to have access now to a replicator that not onlycreates known compositions of matter but also creates new compositionsof matter. The new compositions are created and then tested with resultsbeing fed back into the tradeoff module 118 to make adjustments and theAI 208 module to guide further evaluations. The feedback is also enteredinto the database library module 116 to continue to build the body ofknowledge. Further, in some implementations of the disclosed subjectmatter, the compositions are built first in a virtual environment. TheInstructions of Production are used to run through a simulation module206 of being produced. The AI Module 208 checks that the steps arefeasible and practical. It is envisioned that there may be severalsimilar steps that could be used to progress in the creation of thecomposition of matter and the AI Module 208 would learn which steps arethe most practical. The simulated compositions would be run throughsimulated test procedures to estimate it's assessment. The feedbackwould be cycled into the tradeoff 118 and the AI 208 Modules to evaluatethe entire process. The simulated candidate may be good enough forimprovements in a well understood material. However, an implementationof the disclosed subject matter can also predict the ability to actuallytake the instructions of production that are modified by the tradeoff118 and AI 208 Modules with the simulation module 206 runs and actuallyproduce the new composition of matter with Printer Module 120.

The chemical bench 210 is a chemical reactor system set up to create thenew compounds. One would set up the chemical reactor to be able to addthe constituents as needed for the new compound along with thetemperature, pressure and volume necessary to encourage the correctbonding of the elements to create the desired new matter composition,compound and structure. The chemical bench 210 is envisioned to not onlybe a completely automated wet bench but also have the additionalcapabilities of centrifuge, mixing, adding a current, heating, cooling,volume and pressure adjustments, and additional chemist tools to coaxthe elements to form into the desired chemical compounds. Themethodology of creation is laid out in the Instructions of Productionfrom the QMIP critical processes specifically compiled by the AI Module208.

The 3D Matter printer module 120 will take the chemical bench 210material and then create the chip, circuits, wire, plate, sub-system,system, encapsulate or medical package format as needed. The chemicalbench 210 will create the new materials such as the superconductor,semiconductor, photovoltaics, thermo generator material or drug/medicalsubstance. The material is created and now needs to be put into theproper configuration to be tested.

In an example of the new photovoltaic cells, the chemical bench 210creates the new raw material, which is then formatted by the printermodule 120 into solar cells to be tested. The material is layered onto atest circuit board created by the 3D printer 120 which incorporates thecomputer aided design (CAD) blueprint. Both the raw material andfinished product can then be tested. The newly created raw material canbe tested by a metrology and interferometry module 212 and otherchemical analysis devices within the sample analyzer module 122) tocheck that the newly created compounds fit the Instructions ofProductions, and that the matter/material behaves as envisioned. Successor failure of the created matter/material from the 3D printer module 120are fed back to the tradeoff 118, the database library 116, simulationmodule 206 and artificial intelligence 208 modules.

In the case of an improved Quantum Computer's Josephson Junction, acomputer aided design lays out the circuit design. The QMIP takes therequirements from Input/output modules (110, 112) and finds severallikely candidates. The simulation module 206 verifies the candidates anddesign. The QMIP provides the Instructions for Production and theartificial intelligence 208 module with the tradeoff module 118determine the best candidates. The chemical bench 210 makes the mattersamples in enough quantities to perform the testing of the chemicalproperties in the sample analyzer module 122 that in turn will result ina sample circuit. The printer module 120 takes the candidate materialand builds the test Josephson Junction circuit. The test bench module124 will test the Josephson Junction's functionality.

An additional example is a superconductor for a Tokamak plasma reactor.The requirement (110, 112) is for a superconductor that can be used as awire. The chemical bench module 210 makes various candidates and thematerial is used by the printer module 120 to: extrude thesuperconductor as a wire, a wire that is coated with the superconductormaterial, or as the core filler with a wire sleeve printed around thematerial.

The sample analyzer module 122 is envisioned to test the candidates'structure, chemical properties and candidates' ability to be created inthe correct form factor. The chemical composition of the samples isverified. A metrology and interferometry module 212 or similar devicecan be used to check the candidate's composition of matter was properlymade. There are additional chemical tests that can be used to measureand test the material.

In another embodiment of the disclosed subject matter, the engineer islooking to find a new superconductor to meet certain requirements (110,112). Despite materials like magnesium diboride with a criticaltemperature (T_(c)) of 40 Kelvin, conventional superconductors tend tobe limited by retardation effects. The true high temperaturesuperconductors, at ambient pressure, could be cuprates and ironpnictide. However, these superconductors are not supported or explainedby a quantitative theory of their composition, properties and behavior.Many theorists believe that the attraction interactions that lead to theformation of Cooper Pairs is similar to a magnetic correlation. Onecould develop descriptors of these superconductors and use them topredict new superconductors. The Nosanow Wave Equations can now accountfor the interactions and is the key to develop descriptors to predictnew superconductors. The Nosanow Wave Equations within the NosanowFermion Wave Module 202 can advance the understanding of Cooper Pairs.

FIG. 7 is a flowchart showing details of an example QMIP simulate andconfirm process in accordance with some implementations.

The last steps set forth below in FIG. 10 are to simulate, confirmproperties and use artificial intelligence to improve outputs and theentire system process.

In this example, the engineer is looking for a new superconductor thatwill operate at the critical temperature (T_(a)) greater than 70 degreesKelvin, result in a magnetic field of greater than 10 Teslas, ismalleable and able to be made into an electrical wire used in anelectronic coil for the Tokomak.

Superconducting Material Design Example

In general, the steps for identifying and creating a new superconductoror superfluid can include understanding the range of the desired T_(c)and other desired qualities. Solving for Hamiltonians for the Nosanowequations. Taking derivatives. Completing transformations. Establishingand solving matrices. Determining pressure, volume and energy in termsof temperature to find T_(c). Constructing test material in light ofgiven input parameters and test properties.

For the candidates, some of the requirements include the belowcategories, sub-categories and quantities, according to the embodimentof FIG. 12.

According to an embodiment of the disclosed subject matter, using theQMIP system, the engineer follows the below steps for investigatingpotential superconductors.

The engineer starts with the determination of the critical temperature(T_(c)) as a key variable of several variables. According to oneapproach, the engineer is concentrating on the above requirements atnear normal atmospheric pressure and looking for the highest T_(c).(Often one can achieve a higher T_(c) if you are using an oxygen atom tobond with a metal.) By using the Nosanow Fermion and Boson Wave Modulewhich utilize procedures that are more precise and complete a T_(c) canbe predicted.

In the Requirements phase (110, 112), the engineer needs a metallicsuperconductor from a set of candidates to get not only the highestT_(c) but a result such that it can be used to generate a B-Fieldgreater than 10 Teslas for a Tokamak containment vessel.

Step 5 defines the wave function with the Heisenberg Equation of Motion,a wave description of the particle.

QMIP Quantum Computing Example

Superconducting quantum computers are the leading candidates to realizecomputing capability beyond the reach of today's classical computers.These types of quantum computers thus far hold the greatest promise forthe NISQ, noisy intermediate scale quantum era (non-error correctedqubits) as well as the fault tolerant, error corrected quantumcomputers. Therefore, this use case emphasizes how the QMIP systemenables the development of superconducting qubit modalityimplementations and algorithms throughout the four-phase diagramindicated in Table 2 in accordance with some implementations.

In some implementations, the QMIP system 100 can enhance the accuracy ofthe current classical molecular modeling techniques that use densityfunction theories. This advances near term quantum computer algorithmsimulations (uses AI 208, Nosanow Fermion Wave 202, Simulation module206 Modules, as described in FIG. 1). In some implementations, the QMIPsystem 100 can improve the accuracy of the Variational QuantumEigensolver (VQE) estimates used to help establish a wave function ofmolecule and predict its ground state energy simulations (uses AI 208,Nosanow Fermion Wave 202, Simulation module 206 Modules, as described inFIG. 1).

QMIP enables quantum data compression of complex QMIP qubits as well asmulti-qubit states into one-qubit state thus advancing the use ofquantum auto-encoders (uses AI 208, Nosanow Fermion Wave 202, Simulationmodule 206 Modules, as described in FIG. 1). QMIP provides a library ofonline information that is easily accessed and consumed which stimulatesmarkets to develop that are supported by quantum computing (uses AI 208and database library 116 Modules, as described in FIG. 1).

In stage 3 of Table 2, the QMIP system can be programmed and configuredto reduce errors as it facilitates improved hardware designs at a morerapid pace (uses AI 208, Nosanow Fermion and Boson Wave 202, Grand FreeEnergy 204, Simulation module 206, Metrology & Interferometry 212,chemical bench 210, test bench 124 modules, as shown in FIGS. 1 and 2).The QMIP system can reduce the need for the high ratio of physicalqubits compared to logic qubits, extends coherence time thus enablesmore complex algorithms to run because it provides more exact wavebehavior, as well as a more complex qubit capability with greaterpredictability (uses AI 208, Nosanow Fermion and Boson Wave 202,Simulation module 206, chemical bench 210 and Metrology Modules 212, asshown in FIGS. 1 and 2). The QMIP system can permit functional quantumalgorithms to be developed and implemented faster because of itshardware and qubit advances (uses AI 208, Nosanow Fermion and Boson Wave202, Simulation module 206, Chemical Bench 210 modules, as shown inFIGS. 1 and 2).

In stage 4 of Table 2, the QMIP system is configured and programmed tomore accurately predict quantum phase estimations and quantum parameterswhich leads to dynamic functional simulations of unknown chemicalrelationships for molecules, solids and highly sensitive materials suchas ambient temperature super conductors (AI 208, Nosanow Fermion andBoson Wave 202, Grand Free Energy 204, Simulation Modules 206, as shownin FIGS. 1 and 2). The QMIP system essentially accelerates significantlythe development of quantum computers and algorithms in terms of time andcapability. (Uses database library 116, AI, Nosanow Fermion and BosonWave 202, Grand Free Energy 204, Simulations 206, and Chemical Bench 210modules, as shown in FIGS. 1 and 2) In some implementations, the QMIPsystem can support engineering and material design breakthroughs thatlead to portable and distributed quantum computing.

Today's superconductor Quantum Computers essentially follow a generalprocess cycle as outlined in FIG. 8 and described below. The NISQ (NoisyIntermediate Scale Quantum era) which currently defines mostcommercially accessed quantum computers do not include generalizedquantum error correction procedures such as decomposing errorcorrections into qubits and gates.

FIG. 8 is a diagram showing an example quantum computing control cyclein accordance with some implementations. The five layers of asuperconductor quantum computer include:

The Application layer—where a quantum algorithm is implemented (theprocess of translating a mathematical algorithm into instructions thatare ultimately executed by the hardware), where results are ultimatelyprovided to the user.

The Logic layer—uses the instructions specified by the application layerto prompt the logic gates and ancillas to perform the desired operationswith the least number of gates.

The Error correction Layer—introduces error correction procedures suchas virtual gates that reverse the qubit states; determines errorsyndrome using multi qubit measurements, an error basis, and Pauli gatesto determine where and what type of error occurred.

The Virtual Layer—defines and provides the physical controlinstructions; translates hardware readouts and provides results to theError correction layer.

The Quantum Layer—this includes the actual physical operation of thehardware circuitry components based upon the virtual layer physicalcontrol instructions, and stores quantum computation information andmeasures results to be translated.

There are several key objects, components and principles that underpinthe major elements of a quantum computer, such as:

Qubits—conventionally thought of as two state quantum objects used forinformation storage and processing in a quantum computer.

Qubit spin—the total angular momentum, or intrinsic angular momentum, ofa quantum object.

Qubit measurements—comprise only two possible outcomes (quantum states)for the measurement of a qubit, usually taken to have the value “0” and“1”, like a bit or binary digit. However, the state of a bit can only beeither 0 or 1, the general state of a qubit according to quantummechanics can be a coherent superposition of both.

Quantum computational states—a|0>;b|1> or a|0>+b|1>.

Qubit superposition—represents a quantum computation state that hascoefficients that sum to 1. A pure qubit state is in a coherentsuperposition of the basis states. This means that |ψ>=a|0>+b|1> where|ψ> represents a qubit and where a and b are probability amplitudes thatcan in general both be complex numbers.

Qubit coherence—length of time a qubit remains in a superposition.

Quantum entanglement—a physical phenomenon that occurs when a pair orgroup of particles are generated, interact, or share spatial proximityin a way such that the quantum state of each particle of the pair orgroup cannot be described independently of the state of the others,including when the particles are separated by a large distance.

Quantum parallelism—where each bit string can be computed at once in aquantum computer versus a classical computer that does the samecomputation one bit string at a time, where the coefficients “a” and “b”that form the quantum computation states for the bit strings haveinfinite possibilities α_(i)=A_(i) e^(iθ) ^(i) , which allows problemsto be solved exponentially faster than a classical computer.

Quantum algorithms—step-by-step procedures to perform a calculation, ora sequence of instructions to solve a problem, where each step can beperformed on a quantum circuit which is a model for quantum computation,and where the steps to solve the problem are quantum gates performed onone or more qubits.

Quantum gates—rudimentary quantum circuits operating on a small numberof qubits. They are the analogues for quantum computers to classicallogic gates for conventional digital computers. Quantum logic gates arereversible, unlike many classical logic gates. Some universal classicallogic gates, such as the Toffoli gate, provide reversibility and can bedirectly mapped onto quantum logic gates. Quantum logic gates arerepresented by unitary matrices.

Quantum circuits—models for quantum computation in which a computationis a sequence of quantum gates, that are reversible transformations.

Quantum chip—qubits and quantum circuits often embedded in silicon ormetallic materials.

Quantum read outs—qubit state distributions throughout the course ofprobing the output signal from resonators paired and induced by eachqubit.

Josephson Junctions—a device that sandwiches an ultra-thinnon-superconducting material between two superconductors; electronstunnel through the non-superconducting barrier material resulting in acritical current which if exceeded reduces the critical current resultsin an AC current. When this occurs an oscillation that is anharmonic canbe established which defines electron nonuniform energy states thatultimately allows for a qubit to be interpreted in terms of 1's or 0's.

Transmon—transmission line shunted plasma oscillation qubit; one whichconsists of a Cooper-pair box where the two superconductors are alsocapacitatively shunted in order to decrease the sensitivity to chargenoise, while maintaining a sufficient anharmonicity for selective qubitcontrol.

SQUID—a superconducting quantum interference device constructed from twoJosephson Junctions in a circuit loop that detects very tiny magneticfields.

Quantum computers perform calculations based on the probability of aquantum object's computation state before it is measured. This providesan ability to process exponentially more data compared to classicalcomputers. In quantum computing, the quantum object's state is used toproduce what is known as a qubit. These computation states are theundefined properties of a qubit before they have been measured, such asthe spin of an electron. Rather than having a clear position, unmeasuredquantum states occur in a superposition. These superpositions can beentangled with those of other quantum objects (qubits), meaning theirfinal outcomes will be mathematically related.

The complex mathematics behind these unsettled quantum computationstates of entangled qubits can be programmed into special quantumalgorithms that in turn direct a circuitry of quantum gates (single anddouble) to be established for the qubits to be operated upon. Thisinvolves combinations of Josephson Junctions, Transmons, possiblySQUIDs, and single as well as two qubit gates in a superconductorquantum computer.

Probabilistic readouts based on the phases of electromagnetic wavesemanating from resonators coupled to the qubits are recorded andassessed for error correction. These readouts driven by the quantumalgorithm ultimately provide the quantum computer answer. Suchalgorithms can be useful in solving complex mathematical optimizationproblems, producing hard-to-break security codes, or running simulationspredicting multiple particle interactions in chemical reactions.

Quantum Computing Technical Problems

Building a functional quantum computer requires holding a qubit in asuperposition state long enough to carry out various processes definedby the quantum algorithm error free. Essentially, a qubits' coherencetime needs to be long enough to execute an algorithm thoroughly toreduce errors and provide answers with both accuracy as well asprecision. Today the multi-qubit arrays necessary for quantum computinghave insufficient coherent times and are grounded in a restrictiveunderstanding of quantum mechanics.

Some problems plaguing current implementations of quantum computersinclude quantum computers are error prone at qubit formation; quantumalgorithms require large numbers of single and or double gates toprocess complex problems resulting in longer run times; quantum gatesand circuits have quantum material flaws. Further, Josephson Junctions,Transmons and SQUIDs cause noise and anharmonicity trade off issuesresulting in counterproductive impediments.

Ultimately the applied physics of the prior art is not complete toexplain the needed quantum mechanics requirements necessary to advancequantum computing. Essentially, there are difficulties due primarily toincomplete physics for creating, controlling, connecting, entangling,and processing qubits as well as validating their readouts given theassociated algorithms. The process of setting up the problem, writingthe program, isolating and cooling the quantum computer to createqubits, running the quantum algorithm, collecting the output, correctingthe errors, and interpreting the results are built upon the SchrödingerWave Equation which does not fully explain the quantum object or systembehavior.

Quantum Computing Technical Solutions

The QMIP system fundamentally incorporates the correct kinetic andcomplete potential energy with spin. The QMIP system calculates theoptimum anharmonic wave equation to establish more stable initializedqubits.

The QMIP system provides a computed frequency to rotate qubits intoinitial basis states and as required by an algorithm's defined gates.

The QMIP system defines new superconductors and materials to constructJosephson Junctions, Transmons, SQUIDs, inductors, capacitors,resonators and readout probes that reduce surface material noise. TheQMIP system defines new designs of Josephson Junctions, Transmons andSQUIDs that optimize anharmonic wave increases and electromagnetic noisereductions. The QMIP system defines new circuitry configurations andreduces gate requirements to mitigate errors as well as enable complexproblems to now be addressed. The QMIP system consequently acceleratesthe development of quantum computers and their algorithms to solveproblems that cannot be done by classical computers today.

FIG. 9 is a diagram showing an example quantum computing stack inaccordance with some implementations. According to one embodiment, thequantum computer would be implemented in accordance with the followingquantum computer general stack, including the algorithm layer, quantumprogram language, quantum arithmetic and compiler, quantum instructionset architecture, micro architecture, quantum to classical conversion,and the quantum chip.

Present efforts in quantum computing often focus on three interdependentissues (decoherence, complexity, and errors). These issues in turn drivetoday's approaches in the physics, materials, engineering and techniquesthat underpin the currently constrained quantum computer hardware andalgorithm advancements. The QMIP system uses innovative methodologieswithin its integrated and novel modules to provide breakthroughs in eachof these approaches.

The Nosanow Fermion and Boson Wave 202 and Grand Free Energy 204 modulesprovide a more comprehensive, accurate and predictive behaviorrepresentation of quantum objects versus what currently exists. Unlikethe Schrodinger Wave Equation, the Nosanow Wave Module includes a fourthdimension of spin, accounts for complete kinetic and potential energydescriptions, as well as employs Hamiltonians that can help forecastultimately new superfluids and superconductors.

The Nosanow Fermion Wave 202, Grand Free Energy 204 and the AI 208modules predictively define optimum anharmonic waves that will enablequbits to achieve greater stability and reduce errors associated withstate initialization.

The Nosanow Fermion and Boson Wave 202, Grand Free Energy 204 and the AI208 modules predictively define qubit controllers (microwave and orradio wave frequencies) with exceptional accuracy and precision. Thiswill reduce residual photon surface noise while helping to maintain gatefidelity. As a result, coherence time starts to become greater, thepropensity for errors lessens and the ability to manage complexitybecomes more feasible.

The Nosanow Fermion Wave 202, Grand Free Energy 204, AI 208, Simulation206, and database library 116 modules provide more accurate densitycloud estimates and Density Functional Theories simulations that in turnhelp programmers learn how to develop better quantum simulationalgorithms. These QMIP system modules can also improve VariationalQuantum Eigensolver (VQE) simulations by rendering a more accurate wavefunction ansatz of a molecule and estimations of its electronicHamiltonian while finding its ground state energy. Once again this willhelp programmers learn how to create applicable algorithms for both NISQ(Near Intermediate Scale Quantum) and full-scale fault tolerant quantumcomputers.

The Nosanow Fermion and Boson Wave 202, Grand Free Energy 204, AI 208,simulation 206, chemical bench 210, database library 208, sampleanalyzer 122 and test bench module 124 modules will provide newsuperconductors (descriptions and material) to be used in improveddesigns of Josephson Junctions, Transmons, SQUIDs, inductors,capacitors, resonators and readout probes. This is the result of theQMIP system modules' ability to define thoroughly and then productivelyexploit the phase discontinuity of Fermion systems. This leads tosuperior circuitry with less susceptibility to decoherence due tomaterial and device (charge and magnetic field) created noise.

The QMIP system and other methods and techniques described hereinprovide for, among other things, the following new and improvedtechnologies: new quantum computing topologies such as Fermion BosonQuantum Computers that couple the arrays of Fermion and Boson systems ofqubits, and portable and distributed use of quantum computers.

Nosanow Qubits

New Dense Nosanow Qubits with new quantum numbers that are exploited forprocessing. These new dense qubits will incorporate the use of anentirely new particle, which can be described as a non-relativisticpositron. One non-relativistic particle exists for Fermion. Anotherexits for Bosons.

Dense computing code that goes beyond present use of Boolean logic,including executable error correction and stabilization strategieswithin or coupled to the qubits and gates, and junctions designed toexploit the non-relativistic positron and or new dense qubit. Newlydiscovered superfluids and superconductors whose properties overcome thecurrent quantum computing hardware challenges.

New qubits with greater than 2 states for greater computing power. TheQMIP system is able to create quantum computer qubits and increase theircoherence time.

Qubits are often defined as two state quantum objects used forinformation storage and processing in a quantum computer. The Qubit spin(the quantum number s) is the total angular momentum, or intrinsicangular momentum, of a quantum object. Quantum objects can includeFermions (e.g., electrons), Bosons (e.g., Helium-4) and Quasi-particles(e.g., Anyons).

The Non-Relativistic Quantum Numbers are n—describes the energy level,l—describes the orbital type, m—specific orbital, and s—spin. Qubitmeasurements comprise only two possible outcomes (quantum states) forthe measurement of a qubit, usually taken to have the value “0” and “1”,like a bit or binary digit. However, the state of a bit can only beeither 0 or 1, the general state of a qubit according to quantummechanics can be a coherent superposition of both. Quantum computationalstates can be defined as such: a|>; b|1> or a|0>+b|>.

Qubit superposition represents a quantum computation state that hascoefficients that sum to 1. A pure qubit state is in a coherentsuperposition of the basis states. This means that |ψ>=a|0>+b|1> where|ψ> represents a qubit and where α and b are probability amplitudes thatcan in general both be complex numbers. The Qubit coherence is thelength of time a qubit remains in a superposition.

The Nosanow Wave Equation represents a 4-dimensional system of equations(4 equations) which include S (spin). The Schrodinger Wave Equationrepresents a 3-dimensional system (only one equation) and thus does nothave a 4^(th)-dimension that includes S (spin). This principle aloneenables the Nosanow Wave Equation to describe quantum particles in amore informative and detailed manner. Consequently, the QMIP system isable to construct at least 5 new whole classes of Qubits and quantumnumbers in different dimensions.

The following illustrates a redesign of the core element of quantumcomputing. Unitary transformations can be implemented in a controlledfashion. State changes, readings and writings can be done. Materials,ways to apply control structures and methods to stabilize the NosanowQubits listed below can be realized.

A Nosanow 4-Dimensional and Beyond Qubit is a qubit that can performrequirements of a quantum bit with greater stability gained from a moreinformed understanding of its quantum properties and the means of theirmanipulation. Several of these Qubits could be managed as a FermionSystem array essentially acting as a single complex qubit.

A Nosanow Fermion Positron Qubit is a qubit that can be entangled withan electron qubit by means of a new Josephson Junction designed by theQMIP system. The Nosanow Fermion Positron Qubit can also entangle withother Positron Qubits. It exhibits greater stability and less associatederror within a quantum computer. Again, several of these Qubits could bemanaged as a Fermion System array essentially acting as a single complexqubit.

A Nosanow Positron Boson Qubit is a qubit that can evolve as anasynchronous anharmonic oscillator from which binary computations wouldbe derived. The QMIP system would use the Nosanow Fermion PositronQubits and the Nosanow Boson Wave Equation to initially simulate theoscillator that would then be specified to a Nosanow Positron BosonQubit or a Boson particle/system.

A Nosanow Fermion Quason 1 Qubit is a type of quasi-particle that can beestablished in a solid superconductor. The superconductor and or crystalin conjunction with a strong magnetic field create the phase of matter.The Nosanow Fermion Wave Equations describe the controlled way toperform this function. This Qubit provides significantly improvedcoherence time.

A Nosanow Boson Quason 2 Qubit is a type of quasi-particle that can beestablished in a superconductor and or crystal or glass etc. inconjunction with a strong magnetic field to create the phase of matter.The Nosanow Boson Wave Equations describe the controlled way to performthis work. This Qubit provides significantly improved coherence time.

FIG. 10 is a system diagram showing an environment 1000 including aNosanow qubit and associated components for setting up and maintainingcoherency for the qubit. In particular, a problem to be computed isdefined at 1002. The problem 1002 is then encoded as a quantum algorithmand corresponding instructions 1004.

Materials in a Nosanow quantum computer 1010 are cooled using a coolingsystem 1012. Other parameters or environmental factors of the quantumcomputer can be controlled as well (e.g., pressure and isolation orshielding of the system) to place the Nosanow quantum computer 1010 inan operational state (e.g., superconducting state). Based on the problem1002 and/or quantum algorithm 1004, one or more Nosanow qubits 1006 areset up in the Nosanow quantum computer 1010. The quantum algorithm andcorresponding instructions based are used to control an energy sourcesuch as microwave energy source 1008 and/or a magnetic field accordingto the model of the Nosanow qubits, which includes positive electrons,provided by the Nosanow Wave Equation and/or Hamiltonian.

The quantum computing processing is carried out according to the quantumalgorithm 1004 using the microwave energy 1008. Output 1014 of thequantum computer 1010 is detected (e.g., via one or more resonators) anderror corrected 1016 and output (e.g., as a distribution 1018). By usingthe Nosanow Wave Equation that provide improved Nosanow qubit modeling(e.g., including Josephson junction, modeling of the qubit, and apossibility of having a more efficient quantum computing program due toan increased number of gates that can be included in the circuitry of aNosanow quantum computer), coherency can be maintained longer asdescribed herein and a need for error correction may be reduced byproviding superconducting materials determined using the QMIP systemthat are less prone to noise and errors due to interference (e.g.,materials that can be in the superconducting region at highertemperatures, etc.).

Qubit Setup

With a material selected by the QMIP system as described above, thematerial properties are known. This essentially enables the selection ofhigher temperature superconducting qubits to be realized in a quantumcircuit. The QMIP system provides the capability to readily determinethe ground states of different types of superconductors whichconstitutes the qubit initialization or set-up. In conjunction withdetermining ground states, the QMIP system is capable of determining allother associated quantum properties. These properties lead to otherdimensional qubit initialization possibilities. QMIP greatly enhancesquantum cryogenic circuitry by addressing the balance of control andcoherence time.

FIG. 16 shows set up of a Nosanow qubit through a Josephson junctionutilizing electron tunneling. In particular, an electromagnetic drivercan be controlled to cause an electron to tunnel through the JosephsonJunction resulting in a ket 1 “I1>” state, an electron that does notpass through is in a ket 0 “I0>” state, thus the qubit is initialized.

Regarding qubit set up and control, there are at least three aspects 1)Nosanow qubit set up, 2) control through gates, and 3) control forcoherency. The superconducting material identification, simulation andevaluation described above is related to the set up and control aspects.For example, the Nosanow Hamiltonian and Nosanow Wave Equation can beused to find a new material for a superconductor that can function in abetter way (e.g., through less impurities) as a superconductor comparedto some conventional superconducting materials. A reduction ofimperfections in the material may help reduce noise due toimperfections, which may be a cause of decoherence.

In the Josephson junction, aluminum is typically used because thebarrier has been refined and is more understood. However, othermaterials can be used. By using the QMIP to identify and model a newmaterial that provides a Nosanow qubit capable of being initialized andmoved through gates with greater accuracy (e.g., reducing theprobability of a Nosanow qubit transitioning to an undesired energystate), there is a potential for better control and more stability. Whena system takes into account how the positive electrons affect thecoherency of the qubit, it becomes possible to maintain coherency better(e.g., longer) because the whole system—including positive electrons—istaken into account with the Nosanow Wave Equation. The Nosanow equationstake into account more of the quantum mechanical properties and thusprovide a better understanding of how to initialize and control a systemincluding one or more Nosanow qubits.

When a Nosanow qubit goes through a gate it causes the spin to change.Spin can be thought of as a vector in the center of a sphere (see, e.g.,FIG. 18). When the qubit goes through the gate, gets hit by energy, andthe spin changes, decoherence aspects creep in because the qubit isbeing manipulated. There is uncomputation associated with that forcorrection. For example, the code is changing spin of qubits throughdual gates (e.g., from 0 to 1, etc.). Uncomputation is causing a qubitto go backwards. If positive electrons, as defined in the Nosanowequation models, are used as part of the uncomputation, then a lowererror rate can be achieved because the Nosanow qubit model, as definedby the Nosanow Wave Equations and Hamiltonian, is working with a morecomplete model of the qubit vs an incomplete qubit modeled withconventional wave equations.

Coherency Control

The QMIP system can be programmed and configured to monitor thetemperature (t), pressure (p), and energy (e) as the quantum computerperforms the necessary qubit calculations. The QMIP system can sense andadjust time, pressure, and energy to keep the candidate material of thetransmon/Josephson Junction in the required superconductor zone, therebyextending coherence. The QMIP system can provide the appropriate waveequation to couple multiple qubits (e.g., entangled qubits) that areevolved in time, which allow for coherence to be maintained in order toestablish high probability readouts with minimized error correctionneeds.

As described herein, the QMIP system describes particle and systemquantum mechanical properties more accurately, discovers newsuperconductors which operate at a range of temperatures, and that canbe used to define new and existing material properties.

Some implementations of the QMIP system can be based on the phase changein Tc. The Nosanow Wave Equation explains the properties of the Fermionsystem across the phase changes and discontinuity. Some implementationscan include a process to search for new superconductor materials andleverage the properties of new material enhance coherence performance byproviding a material with a higher probability of coherent operationwithin a circuit or system.

FIG. 17 is a diagram showing a diagram showing an example Nosanow qubitand associated drive and resonator components. In operation, XY drive1702 drives gate operations, as visualized on the Bloch Sphere (see,FIG. 18) as the amount of rotation and the axis of rotation. A Nosanowqubit 1704 oscillates between the ground and first energy level states(as shown in the dashed line in the energy level diagram 1708).

Flux-bias 1706 drives the Nosanow qubit between the two energy states(see dashed rectangle in the energy level diagram 1708) and helpsmaintain coherence. Energy level diagram 1708 defines the change in theenergy levels as a bit (in this case as a Nosanow qubit).

FIG. 18 is a diagram of a drive and Nosanow qubit and shows qubitcontrol using an electromatic wave pulse defined by the Hamiltonian. Forexample, the Nosanow Hamiltonian can be used as an operator inconjunction with the Nosanow Wave Equation to predict how the qubit willreact (e.g., amount of rotation and axis of rotation) to a given energyfrequency.

FIG. 19 is a diagram showing an example of a Nosanow paired qubit andassociated drive component in accordance with some implementations. Inparticular, FIG. 19 includes a schematic of an XY drive circuit and Zdrive for a qubit along with a readout resonator. General qubit stateand state probabilities equations are also shown in FIG. 19.

Moving back to FIG. 11, FIG. 11 is a diagram showing an examplesuperconductor relationship between temperature, current density, andmagnetic field in accordance with some implementations. FIG. 11 shows aregion where the superconductor is active. In some implementations, theQMIP system can optimize or control the three variables to establishconditions for a superconductor.

FIG. 12 is a diagram of an example QMIP system configured for industrialprocess control and optimization and an associated environment inaccordance with one or more implementations described herein. Inoperation, the QMIP system is programmed and configured to control andoptimize the industrial process based on monitoring one or moreparameters of the industrial process.

Other QMIP Example Applications Thermoelectric Generator

Improve the material for thermoelectric generators. Thermoelectricgenerators create electricity due to the Seebeck effect and thedifference between a heat source and a cold sink. When there is a dieselgenerator, the generator creates waste heat. Thermoelectric generatorsconvert the waste heat into electricity, making the generators moreefficient. Thermoelectric generators have been used in generators,automobiles and space probes.

According to Wikipedia:

-   -   “An automotive thermoelectric generator (ATEG) is a device that        converts some of the waste heat of an internal combustion engine        (IC) into electricity using the Seebeck Effect. A typical ATEG        consists of four main elements: A hot-side heat exchanger, a        cold-side heat exchanger, thermoelectric materials, and a        compression assembly system. ATEGs can convert waste heat from        an engine's coolant or exhaust into electricity. By reclaiming        this otherwise lost energy, ATEGs decrease fuel consumed by the        electric generator load on the engine. However, the cost of the        unit and the extra fuel consumed due to its weight must be also        considered.”    -   Wikipedia also states the following regarding efficiency of        ATEGs:

Currently, ATEGs are about 5% efficient. However, advancements inthin-film and quantum well technologies could increase efficiency up to15% in the future.^([5])

The efficiency of an ATEG is governed by the thermoelectric conversionefficiency of the materials and the thermal efficiency of the two heatexchangers. The ATEG efficiency can be expressed as:

ζ_(OV) = ζ_(CONV) × ζ_(HX) × ρ

Where:

-   -   ζ_(OV): The overall efficiency of the ATEG    -   ζ_(CONV): Conversion efficiency of thermoelectric materials    -   ζ_(HX): Efficiency of the heat exchangers    -   ρ The ratio between the heat passed through thermoelectric        materials to that passed from the hot side to the cold side

Wikipedia goes on to state the problems and challenges facing ATEGs?

-   -   “The greatest challenge to the scaling of ATEGs from prototyping        to production has been the cost of the underlying thermoelectric        materials. Since the early 2000s, many research agencies and        institutions poured large sums of money into advancing the        efficiency of thermoelectric materials. While efficiency        improvements were made in materials such as the half heuslers        and skutterudites, like their predecessors bismuth telluride and        lead telluride, the cost of these materials has proven        prohibitive for large-scale manufacturing. Recent advances by        some researchers and companies in low-cost thermoelectric        materials have resulted in significant commercial promise for        ATEGs, most notably the low-cost production of tetrahedrite by        Michigan State University] and its commercialization by US-based        Alphabet Energy with General Motors.”

The QMIP platform can address the challenges of ATEGs to provideworkable solutions. The QMIP can be used to study the Seebeck effect andextend it to additional uses with more efficient materials. As long asone has a heat difference, the Seebeck effect can be used to createelectricity.

Solid-State Electronics

Solid-state electronics is an additional area for the QMIP to be used.Advancements in better semi-conductors can lead to longer lasting chips,components and systems.

For example, Wikipedia states:

-   -   “Semiconductor materials are nominally small band gap        insulators. The defining property of a semiconductor material is        that it can be doped with impurities that alter its electronic        properties in a controllable way. Because of their application        in the computer and photovoltaic industry—in devices such as        transistors, lasers, and solar cells—the search for new        semiconductor materials and the improvement of existing        materials is an important field of study in materials science.    -   Most con only used semiconductor materials are crystalline        inorganic solids. These materials are classified according to        the periodic table groups of their constituent atoms.    -   Different semiconductor materials differ in their properties.        Thus, in comparison with silicon, compound semiconductors have        both advantages and disadvantages. For example, gallium arsenide        (GaAs) has six times higher electron mobility than silicon,        which allows faster operation; wider band gap, which allows        operation of power devices at higher temperatures, and gives        lower thermal noise to low power devices at room temperature;        its direct band gap gives it more favorable optoelectronic        properties than the indirect band gap of silicon; it can be        alloyed to ternary and quaternary compositions, with adjustable        band gap width, allowing light emission at chosen wavelengths,        which makes possible matching to the wavelengths most        efficiently transmitted through optical fibers. GaAs can be also        grown in a semi-insulating form, which is suitable as a        lattice-matching insulating substrate for GaAs devices,        Conversely, silicon is robust, cheap, and easy to process,        whereas GaAs is brittle and expensive, and insulation layers        cannot be created by just growing an oxide layer; GaAs is        therefore used only where silicon is not sufficient.^([2])    -   By alloying multiple compounds, some semiconductor materials are        tunable, e.g., in band gap or lattice constant. The result is        ternary, quaternary, or even quinary compositions. Ternary        compositions allow adjusting the band gap within the range of        the involved binary compounds; however, in case of combination        of direct and indirect hand gap materials there is a ratio where        indirect band gap prevails, limiting the range usable for        optoelectronics; e.g., AlGaAs LEDs are limited to 660 am by        this. Lattice constants of the compounds also tend to be        different, and the lattice mismatch against the substrate,        dependent on the mixing ratio, causes defects in amounts        dependent on the mismatch magnitude; this influences the ratio        of achievable radiative/nonradiative recombinations and        determines the luminous efficiency of the device. Quaternary and        higher compositions allow adjusting simultaneously the band gap        and the lattice constant, allowing increasing radiant efficiency        at wider range of wavelengths; for example, AlGaInP is used for        LEDs. Materials transparent to the generated wavelength of light        are advantageous, as this allows more efficient extraction of        photons from the bulk of the material. That is, in such        transparent materials, light production is not limited to just        the surface. Index of refraction is also composition-dependent        and influences the extraction efficiency of photons from the        material.”

Photovoltaic Example

Solar energy has always been a green solution that has promise but whichhas no widespread success because it is hampered by low efficiency.Sunlight is a fickle source that only produces less than half thepotential electrical energy during daylight. Greater efficiency, cheapercosts and greater longevity of the solar cells will lead to greateracceptance. However, one also needs a greater storage capability, thatis, a better battery, to store the electrical potential. Along withbetter photovoltaics, the QMIP system can be used to find better storagealternatives for the created electricity. Photovoltaics are multilayercells exhibiting an efficiency in the range of roughly 30%-45%. In someimplementations, the QMIP platform according to FIGS. 1 and 2 can beprogrammed and configured to identify and develop improved photovoltaicmaterials and systems.

Quantum Chemical Design Example

The design workflow for the design of quantum chemicals is set forth inTable 3 and in the steps and activities described further below. Atstages 1 and 2 of Table 3, the QMIP system can be programmed andconfigured to manage large amounts of data about small molecules,polymers, solids like composites, semiconductors, magnetic materialsetc. (uses database library 116 and AI 208 modules). QMIP enhancescurrent DFT, CALPHAD and VQE (Variational-Quantum-Eigensolver)calculations and can provide more accurate predictions of molecularproperties (uses AI 208, Nosanow Fermion Wave 202, and simulation 206modules)

In some implementations, a QMIP system is configured to provide moreaccurate predictions of new solid-state materials and their propertiessuch as magnets, superconductors, semi-conductors and battery materials(uses AI 208, Nosanow Fermion Wave 202, Grand Free Energy 204,Simulation module 206, chemical bench 210, Metrology & Interferometry212, Printer 120 modules).

The QMIP system can manage large amounts of data about small molecules,polymers, solids like composites, semiconductors, magnetic materialsetc. (uses database library 116, AI Modules 208). QMIP enhances currentDFT, CALPHAD and VQE (Variational-Quantum-Eigensolver) calculations andcan provide more accurate predictions of molecular properties (uses AI208, Nosanow Fermion Wave 202, Simulation module 206 Modules)

QMIP is able to provide more accurate predictions of new solid-statematerials and their properties such as magnets, superconductors,semi-conductors and battery materials (uses AI 208, Nosanow Fermion Wave202, Grand Free Energy 204, Simulation module 206, chemical bench 210,Metrology & Interferometry 212, Printer 120 modules).

In stages 3 and 4 shown in Table 3, the QMIP system is programmed andconfigured to provide insights involving complex processes and useoptimization for formulation development of mixtures (such as materialsfor charge transport in OLEDs) (uses AI 208, Nosanow Fermion Wave 202,Grand Free Energy 204, Simulation module 206 Modules). The QMIP systemis also able to test mixtures (such as detergents and pesticides)integrating in-silico with automated physical measurement procedures (AI208, Metrology & Interferometry 212, chemical bench 210, and test benchmodule 124 modules). QMIP is able to test complex assemblies (such ascomposites, OLED stacks and quantum dots (uses AI 208, Metrology &Interferometry 212, printer module 120, and test bench 124 modules).

In stages 5 through 7 of Table 3, the QMIP system is able to predict andsimulate new molecular catalyst designs that reduce reactor temperatureand or pressure requirements (such as new non-iron based catalysts)(uses database library 116, AI 208, Nosanow Fermion Wave 202, Grand FreeEnergy 204, Simulations 206, and chemical bench 210 Modules). QMIPimproves and optimizes reactor set-ups (uses AI 208, Simulation module206, Nosanow Fermion Wave 202, and Grand Free Energy 204 Modules). QMIPis able to identify non-intuitive data correlations to fine tune processconditions that reduce by-products but increase yields. (AI 208,Simulation module 206, tradeoff 118, database library 116 Modules)

A chemical engineer has in many cases several challenges when given anew project. He uses an inexact science to solve his problem. Manyprojects take substantial trial and error, experimentation and time thatinvolves different lots with significant variability. Time intensiveresearch is often expensive often depends on the experience of thechemical engineer who typically is just lucky to formulate the rightchemical combinations to solve a problem or improve a product.

There may not exist an integrated quantum mechanics instructionproduction system and platform that uses quantum mechanics to predict,develop, improve, or understand chemical properties and compounds tosolve chemical engineering problems.

In operation, an implementation of the QMIP system can be programmed andconfigured to perform operations including: take the inputs for thefunctional requirements (110, 112) for the desired chemical propertiessolution(s) and non-functional requirements for the manufacturabilityand performance criteria, uses the license and authorization securitymodule 114 to check the user's access, clears the search, establishessearch and usage limits, and protects the user's discoveries as needed,uses Artificial Intelligence 208 to assist the processes and learn howto make the processes more effective, searches the Database 116 forexisting and starting solutions, applies the QMIP system core processmodules: Grand Free Energy 204, Nosanow Fermion Wave Equation 202, andArtificial Intelligence 208 Modules, to develop candidates, uses theTrade-Off module 118 to rank the candidates to the Requirements of theInput/Output Modules (110, 112), uses the Simulation module 206 to checkcreation of the candidates and the needed chemical properties, uses theChemical bench module 210 to create candidate compounds, uses thePrinter Module 120 to put the candidate compounds in the correct form,uses the sample analyzer module 122 to test the compounds to ensure thecorrect molecules are made, uses the Test Bench Module 124 to ensure thecandidates can fit in the needed form factor, uses the Metrology andInterferometry Module 212 to measure the candidate's properties inconjunction with the sample analyzer module 122 and/or the Test benchmodule 124 Modules, uses the Tradeoff module 118 to evaluate thecandidates results, routes candidates' properties into the Database 116,updates the Artificial intelligence module 208 with a new instruction,uses the License and Authorization Security Module 114 to log the user'saccess, uses the License and Authorization Security Module 114 topartition and safeguard user's findings/discoveries as needed, theInput/output modules (110, 112) receives the results to include thecandidates' instructions for production, structural formulation toinclude bonds, test results, Hamiltonians, Requirements Scorer, TradeoffScaler, the appropriate frequency, and what is needed to control thecreation, composition, and further manipulation of the compound and theinteraction, and creates the candidate samples using the chemical bench210 and printer module 120 Modules.

In some implementations, a chemical engineer has an integrated QMIPsystem to assist with his/her projects. In this example, the chemicalengineer will be improving an organic light emitting diode (OLED) for anew screen to put into a new mobile phone release. OLEDs have advantagesover crystal LEDs as OLEDs do not need backlighting, which reduces powerrequirements. However, the cost of OLEDs are high and production yieldsare low. This is a textbook case of using QMIP to improve a knownstarting point.

The chemical engineer gains access to the QMIP and has an accountthrough the License and Authorization Security Module 114. He enters theRequirements into the Input/output modules (110, 112). His requirementsinclude both functional (i.e., color, brightness, responsiveness,slimness, flexibility) and non-functional requirements (i.e., yield andmanufacturability). The Artificial Intelligence Module 208 assists byfirst searching the database library module 116 for already existingsolutions. These starting solutions are then checked to see if theysatisfy the problem using the Tradeoff module 118.

If the Database Library Module 116 search has solutions that satisfy theTradeoff 118 requirements, then the Input/output modules (110, 112)signals that samples are to be printed (e.g., using 120), adjusted forform factor and then tested.

If the chemical engineer believes or the Artificial Intelligenceindicates the Database Library does not have the appropriate solution,then the Artificial intelligence module 208 involves the Nosanow FermionWave 202 and the Grand Free Energy 204 Modules to find “organicchemical” and/or “non-organic” compounds that satisfy the inputrequirements. The Artificial Intelligence can learn and supervisethousands, millions and billions of different alternative compounds forthe better alternatives. The better candidates are stored in the LibraryDatabase Module 116, the tradeoff module 118 evaluates the candidatesversus the Requirements 110. The better candidates are virtuallyconstructed in the Simulation Module 206 to check that the candidatesare able to be actually produced and performed as expected. TheArtificial Intelligence 208 directs the Simulation module 206 module andinstructs from past failures. These failures could include possiblechemical compounds that are theoretically possible but have proven tolack stability. The Simulation Module 206 informs the instructions forproduction now developed by the AI Module 208 to be the blueprint forthe creation of the compound. Again, actual compoundinformation/properties with context are provided as input into theTradeoff module 118 to be evaluated and supported by the AI Module 208.The analysis could be stopped at this time with the solutions beingadequate for the requirements.

The chemical engineer employing the, AI 208 and/or the tradeoff module118 could begin the chemical bench 210 process to actually create thecandidate compounds. According to one embodiment, the chemical bench hasappropriate components such as one or more automated chemical reactors,raw materials (i.e., elements and basic compounds), stirrers,temperature adjusters, pressure and volume adjusters, centrifuges, andassaying capabilities, etc. to create the candidate compounds. Thecandidates are created and tested by the sample analyzer module 122 toensure the proper compounds are made. The created samples are used bythe Printer Module 120 to put the chemical samples in the correct formfactor to be tested by the Test Bench Module 124. In this example, theOLED candidates are built into the sample OLEDs mobile phone displaysand the Test Bench Module 124 tests the mobile phone displays. Both theSample analyzer module 122 and the Test bench module 124 Modules areconnected to the Metrology and Interferometry Module 212.

The Chemical bench module 210 is envisioned to be an auto wet bench,used to distribute, and dispense chemicals and mix and blend thesubstances for reaction. The Chemical Bench can perform drying, cleaningsurfaces, stripping material surface, texturing, etching semiconductorsand electroplating. It could also include quantum chemistry calculationsthat would tie into AI 208, Simulation module 206 and sample analyzermodule 122 Modules.

The Metrology & Interferometry (M&I) Module 212 is envisioned to use notjust light waves, but also matter wave interference patterns, to measuresmall displacements, indexes of refraction, and surface irregularities.Examples are optic fiber quality and matter surface impurities. The M&IModule 212 could also be used to support spectroscopy that determinesdifferent substances. The Metrology & Interferometry Module 212 linksinto the AI 208, sample analyzer module 122 and chemical bench 210Modules.

The chemical bench 210 and Sample analyzer module 122 Module isenvisioned to include an automated Gas Analytic Chromatography and MassSpectrometry system that combines features of each system to identifysubstances within a test sample (e.g., 214). The Chromatographysub-system takes small amounts of material and then establishes thepresence and proportion of components. The Mass Spectrometry sub-systemdetermines element and isotope signatures. The sample analyzer module122 links into the AI 208, Chemical Bench 210, and Metrology &Interferometry module 212.

The Artificial Intelligence 208, Sample analyzer module 122, Test benchmodule 124 and the Metrology and interferometry module 212 Modulesreport-in their results for each candidates' attributes to the Trade-offModule 118. The tradeoff 118 and AI 208 Modules continue to evaluate thecandidates for the best fit of functional and non-functionalrequirements. The Tradeoff module 118 can be envisioned working inseveral different ways. One method is to find the optimal solution thatbest fits the requirements. Another method is to find all the candidatesolutions that work and identify all the candidates that did not workand the reasons for the failures. Sometimes, the most important insightsinvolved are identifying the candidates that do not work and learningthe reasons why. The results of the Tradeoff module 118 are passed alongto the Input/output modules (110, 112) to be displayed to the chemicalengineer and stored in the database library module 116. The chemicalengineer can check the results and decide if he has found an acceptableanswer or if there are multiple acceptable solutions, if there are noacceptable responses or if changes are needed such asnarrowing/broadening of the search.

It is envisioned that the chemical engineer will have the capability todo sequential iterations of searches. For the first part of the quantummechanics search, the chemical engineer may want to see if the QMIPsystem can achievably produce better candidate compounds than thestarting solutions. The QMIP system would essentially take a new novelidentify candidate solution and then use the Simulation Module 206 tocheck if and under what conditions it could be produced. Thisinteraction run of QMIP would find candidate compounds for thefunctional requirements without consideration for the non-functionalrequirements and without producing chemical samples or printing thesamples in the correct form factor.

The chemical engineer's first attempts are to define the solution'sspace. In this example case, there were several hundred possiblecandidates discovered. A next set of iterations for the QMIP systeminvolves the chemical engineer adding in non-functional requirementssuch as manufacturing and performance factors. The QMIP system takes theprevious identified candidates and then evaluates both the functionaland non-functional requirements. The Simulation module 206, tradeoff 118and AI 208 Modules are actively engaged as the candidates are checkedagainst the full set of requirements. The results are displayed to thechemical engineer, who decides if additional changes in the requirementsare needed.

Continuing with the OLED case, the engineer has found 10 worthycandidates that can proceed into the Chemical bench module 210. Theinstructions for production and the raw materials are provided to thechemical bench and the compounds are created. Through the use of theSample analyzer module 122 and the metrology and interferometry module212 modules, the candidates are tested to ensure the correct chemicalcompounds were created. As stated in the materials design case example,there are a variety of spectrometry and other chemist tools deployed totest the candidates. The AI module 208 advances in its capabilities frommistakes such as when candidate samples are not created as expected onceanalyzed. The candidate samples that are made correctly and have thechemical properties as expected are then passed to the 3D printer module120.

The 3D printer module 120 will take the candidate material and form theOLED material to be used in the test display. In this example, thechemical engineer is using the present OLED as the champion candidateand the new candidates as challengers. The chemical engineer has theQMIP create and then print the samples. The Samples Analyzer tests thesamples for their formulation in accordance with the instructions forproduction. He then compares the different samples using the test benchmodule 124 and the tradeoff module 118 employing the champion versuschallenger strategy. The tradeoff module 120 rank orders the candidatesand the results are passed to the input/output (110, 112) and thedatabase library 116 modules. At this time, the chemical engineer hasfound several candidates that provide better brightness, durability,plus provides better cost and manufacturability then the presentchampion screen OLED.

The QMIP displays the results in the Input/output modules (110, 112) forthe chemical engineer to make notes and prepare the output into theproper format he desires. The chemical engineer stores the results inthe database library module 116 to be able to call up the results whenhe writes up his analysis, constructs his management presentation,writes a technical paper for publication and/or files a patentapplication for one or more of the candidates.

According to one embodiment, the QMIP will not just create instructionsfor the small batch sizes for analysis, 3D printing and test. QMIP willalso create instructions for production for large scale fabrication ofthe composition of matter in a streamlined and optimized method. Theinstructions for production can be modified according to the facilityand cost of the raw materials, energy consumption, and limitations ofthe facility. For instance, the cost of energy may be prohibitivelyexpensive, so a different formulation would be recommended enabled by adifferent catalyst to lower energy costs with acceptable tradeoffs. FIG.18 illustrates the application of the QMIP system and process to thelarge-scale production of materials.

In a different example, the chemical engineer is given material toconduct an analysis. In the following diagram, he can use the QMIP to doan analysis and determine the composition of the sample. Thesignificance is QMIP can analyze the sample, determine the quantumcomponents, and then reproduce additional amounts of the sample. Theadditional amounts can be used by the 3D Printer Module 120 to fit newcircuits or in form factors. The performance of the material from theSample analyzer module 122 and the Test bench module 124 Modules isassessed and fed into the Input/output modules (110, 112) to become theRequirements. This feature of the QMIP system analyzes the sample,creates additional matter, prints the matter into the test circuit,leverages the Sample analyzer module 122, Test bench module 124 and theMetrology and interferometry module 212 Modules to essentially validatethe Requirements. The AI Module 208 can improve on the original sampleby checking the database library module 116 and/or performing iterationsusing the QMIP Critical Processes.

The process of conducting the analysis on the champion or originalsample first is a novel way of getting the starting solution's quantumtechnical requirements without the chemical engineer's having to guesson requirements in areas that are unknown or beyond his currentknowledge.

As the database library 116 Module grows, the chemical engineer will beable to start his analysis with the desired champion sample already inthe database library 116 Module. The chemical engineer could start theanalysis process and discover the champion sample is already in thedatabase library 116 Module.

The chemical engineer example above could also describe other “usecases” such as the material design, solar cell example or the medicalresearcher's case that follows further in this document. Nothing in thispatent application should be restrictive to one type of use case. Giventhe quantum mechanics resources of the QMIP system, the confluence ofmaterial design, chemical engineering, medical design, quantum computingand financial optimization scenarios represented in this provisionalapplication is intentional and speaks of the larger concept of quantumconvergence and quantum emergence. Emergence of the methodicalself-organization of complex (systems, products, technologies) havingproperties unlike any of its constituents. QMIP will expand theunderstanding of how to apply quantum mechanics in our everyday lives(materials, compounds, composites, technologies, etc.).

For example, in some implementations, a QMIP system can takerequirements and forecast compounds that best match the requirements.The QMIP Tradeoff module 118 balances functional and non-functionalrequirements in the generation of alternative compounds for a championmaterial. The QMIP Tradeoff module 118 balances functional andnon-functional requirements in the generation of alternative compoundsfor a specific purpose. The QMIP forecasts new compound's chemical andelectromagnetic properties.

In some implementations, the QMIP can take functional and non-functionalrequirements as inputs and forecast new compounds whose chemical andelectromagnetic properties fit the requirements. The QMIP system is alsoable to conduct a tradeoff analysis on the candidate compounds given aset of requirements.

The QMIP system can be configured to analyze samples, determinecomposition and create more of samples. The QMIP system can analyzesamples and create requirements. The QMIP system can use a sample'srecord from the database library module 116 to begin the requirementsand tradeoff processes.

In some implementations, the QMIP system can take a sample, determinethe quantum composition of the sample, and create instructions forproduction. The QMIP system can create instructions for production forboth small batches and/or for large industrial constructions. The QMIPsystem can be configured to monitor industrial processes and makechanges to optimize the cost or yield of the process.

In some implementations, a QMIP system can be used to iteratively findcandidates (e.g., materials, superconductors, etc.) that fit thefunctional requirements as well as the sub-set of candidates that fitboth the functional and non-functional requirements.

Low Energy Nuclear Reaction Example

In Andre Rossi's patent, U.S. Pat. No. 9,115,913 entitled “FluidHeater”, he disclosed the catalyst and fuel used to achieve his LowEnergy Nuclear Reactor (LENR) that runs his E-Cats. Rossi claims thatonce his fuel is heated, then the system produces enough heat to runsteam turbines. Unfortunately, the alleged solution of Rossi isembroiled in controversy, doubts, claims of fraud and legal problems.Does that system work? Why does it work? One can believe that Rossi doesnot have the chemistry to show why that system works and that inventor'ssecretive and combative nature makes his alleged discovery almostimpossible to be taken seriously.

In an example application, a QMIP system can show if Rossi's system isplausible or not and if possible, then the QMIP system can find bettermaterials to run the process and create heat to run steam turbines. On asmaller level, LENR could create hot water for heating swimming pools,commercial laundry operations, and buildings or homes.

Rossi's patent (U.S. Pat. No. 9,115,913), describes: “The entire set oflayers is welded together on all sides 15 to form a sealed unit. Thesize of the wafer 32 is not important to its function. However, thewafer 32 is easier to handle if it is on the order of 3-inches-thick and12 inches on each side. The steel layers 50, 52 are typically 1 mmthick, and the mica layers 40, 48, which are covered by a protectivepolymer coating, are on the order of 0.1 mm thick. However, otherthicknesses can also be used.

In operation, a Voltage is applied by the Voltage source 33 to heat theresistor 42. Heat from the resistor 42 is then transferred by conductionto the fuel layers 54, where it initiates a sequence of reactions, thelast of which is reversible. These reactions, which are catalyzed by thepresence of the nickel powder, are:

3LiAlH₄ → Li₃AlH₆ + 2Al + 3H₂ 2Li₃AlH₆ → 6LiH + 2Al + 3H₂2LiH + 2Al → 2LiAl + H₂

Once the reaction sequence is initiated, the Voltage source 33 can beturned off, as the reaction sequence is self-sustaining. However, thereaction rate may not be constant. Hence, it may be desirable to turn onthe voltage source 33 at certain times to reinvigorate the reaction. Todetermine whether or not the voltage source 33 should be turned on, thetemperature sensor 37 provides a signal to the controller 35, which thendetermines whether or not to apply a Voltage in response to thetemperature signal. It has been found that after the reaction hasgenerated approximately 6 kilowatt hours of energy, it is desirable toapply approximately 1 kilowatt hour of electrical energy to reinvigoratethe reaction sequence. Eventually, the efficiency of the wafer 32 willdecrease . . . ”

Pharmaceutical Design Example

Table 4 illustrates the steps and workflow for drug design using theQMIP platform and methods.

As shown in stage 1 of Table 4 a QMIP system manages large amounts ofdata about cell signaling and disease drivers. The QMIP system providesgreater and faster computing power to run complex algorithmsefficiently. The QMIP system creates more efficient complex algorithmsthat identify unique signaling and associated disease drivers.

At stage 2 of Table 4, the QMIP system manages large amounts of dataabout the human system. The QMIP system provides greater and fastercomputing power that run complex algorithms efficiently. The QMIP systemcreates more efficient complex algorithms that reflect the human systemmore completely.

At stage 3 of Table 4, the QMIP system manages large amounts of datafrom virtual libraries. The QMIP system provides greater and fastercomputing power that run complex algorithms efficiently. The QMIP systemcreates more efficient complex screening algorithms that discover uniqueinformation and knowledge for test development.

At stage 4 of Table 4, the QMIP system manages large amounts of datafrom virtual libraries. The QMIP system provides greater and fastercomputing power that run complex algorithms efficiently. The QMIP systemcreates in-depth complex screening algorithms that identify toppotential chemical compounds.

At stage 5 of Table 4, the QMIP system manages large amounts of dataabout the properties of valued chemical compounds. The QMIP systemprovides greater and faster computing power that run complex algorithmsefficiently. The QMIP system creates complex algorithms that optimizechemical compounds as designs for candidate drugs.

At stage 6 of Table 4, the QMIP system manages large amounts of dataabout chemical compounds and human cell metabolism and organ toxicologyetc. The QMIP system provides greater and faster computing power thatrun complex algorithms efficiently. The QMIP system creates complexalgorithms that predict the human system in terms of drug actions suchas absorption, distribution, metabolism, elimination and resultingtoxicities.

At stage 7 of Table 4, the QMIP manages large amounts of data specificto drug testing. The QMIP system provides greater and faster computingpower that run complex algorithms efficiently. The QMIP system createscomplex algorithms that simulate drug patient interactions that definepossible efficacy-safety balance and biological system profile.

At stage 8 of Table 4, the QMIP system manages large amounts of dataspecific to drug testing. The QMIP system provides greater and fastercomputing power that run complex algorithms efficiently. The QMIP systemcreates complex algorithms that analyze results and definesefficacy-safety balance, therapeutic index scores and biological systemprofiles.

A conventional process for pharmaceutical design can include:

Identifying the disease drivers

Targeting validation that the drug could work on the disease

Developing tests to measure the target impact

Identifying promising compounds

Optimizing the potential drugs and select a drug candidate

Studying metabolism, toxicity, etc.

Testing humans for efficacy, safety, toxicity and dosage level, and

Submitting for approval.

Current drug testing is complicated, time consuming, expensive and has ahigh failure rate. An incomplete matter and energy model lead to grossapproximations of how molecules, composition of matter, and/or drugsexist, interact and affect each other in a human body or organism. Thisleads to great difficulty in identifying the disease drivers, validatingthe target, determining the impact, tradeoffs among effectiveness,dosage, toxicity, and efficacy.

The lack of understanding of molecular structures and interactions cancause difficulties for recognitions, predictions and manipulations.

Current theory does not adequately describe quantum behaviors and energytransfers of the valence clouds. This lack of knowledge bewilders thedrug investigators as they build on the incomplete body of knowledge todescribe ever increasing complex relationships.

Adding greater variables of time and expense without a model for thedrug's theoretical effects versus field-trial effectiveness allow roomfor very significant improvements. Understanding the chemical reactionsand the additional improvements of using better quantum computers(enhanced understanding, components, chips, and hardware) enabled byQMIP can speed up calculations. The QMIP improved Quantum Computer canuse quantum algorithms to model the effect of a single drug on a virus,the effects on cells, organs, body, functional conditions andenvironment. The AI Module 208 adds the insights and calculations to theQMIP system body of knowledge that enables future investigations to becompleted at reduced time and expense.

The Quantum Mechanics Instruction Production (QMIP) system determines adrug's profile's effect on targeted diseases and the drug's effects onnon-targeted organisms.

The QMIP system database library 116 in conjunction with previous work,allows for a conceptional advancement of quantum particle, chemicalbond, matter, energy, element, structurally formulated molecule, anddrug performance. QMIP constructs drugs, enzymes, proteins, andcarbon-based systems.

The QMIP system can provide a better plot of drug interactions within anorganism's system. A drug may be targeted against Disease A. QMIP isable to demonstrate the drug's usefulness against Disease A as well aswarn how the drug will affect the rest of the cells, biological systems,subject and environment. As an example, suppose Drug A is identified asa great way to fight Disease A, but may have an adverse effect on nervesor the digestive system. QMIP builds and learns about the drugperformance on Disease A, inclusive of toxicity tradeoffs or sideeffects outside targeted parts of the body. QMIP attains the properdosage and distribution of Drug A to maximize the effect on Disease A,while minimizing adverse side effects. The QMIP AI Module 208 recognizesthe chemical signaling done by the cell functions of other cells.

As an example, cell signaling culminates in the electro-chemical processof the cardiovascular system directing the heart how to pump bloodthrough chambers opening and closing valves. Such a systems can besusceptible to conditions not unlike heart arrhythmia.

The QMIP system utilizes the database library 116 to build systemprofiles. A profile is a description of the matter, energy, potentialenergy transfers, bonds, interactions, catalysts, inhibitors, behaviors,characteristics, tendencies, effectiveness, predictions, dosage, sideeffectiveness, cell signaling, Efficacy-Safety Balance and theTherapeutic Index score.

Profiles can include: quantum particles; elements; molecules; types ofbonds (e.g., ionic bonds, covalent bonds, polar bonds and hydrogenbonds); composition of matter; drugs; enzymes; proteins; DNA and RNA;cell components (e.g., tissue, nuclei, etc.); cells (e.g., muscle,nerve, red blood cell, etc.); multi-cell collections that perform aspecific function (e.g., attach to bone or restricts blood flow, etc.);organs and their tissues (can be broadly categorized as parenchyma, thetissue peculiar to (or at least archetypal of) the functional purpose ofthe organ and stroma, the tissues with supportive, structural,connective, or ancillary functions; system of organs such as thecirculatory system or digestive systems; entity (human, plant, animal,insect, synthetic entity, etc.); collection of entities (e.g., howtreating a person in a community impacts the health of the community);and environments individuals live in.

Tradeoffs can be done per an efficacy scale, intensity ofresponse/individual target and non-target, dosagerequirements/individual target and nontarget, toxicity/individual targetand non-target. Target is the specific disease, cell, living matter,organ, organism, human, animal, plant, synthetic organism, environment,etc. The target is what a technique seeks to influence. Non-target isnon-specific disease, cell, living matter, organ, organism, human,animal, plant, synthesized organism, environment, etc. The non-targetscorrelate to the side effects that could involve organisms that were notbeing targeted.

Quantum Medical Research Example

A medical researcher will use the QMIP system to develop new treatmentsfor disorders. In this scenario, a disease has caused a weak signal tomanifest itself and cause chemical imbalances to make the heart misfire.The medical researcher has identified the disease driver with the weaksignaling and chemical imbalance and begins to do target validationusing the Virtual Medical Simulator (VMS) 1500. Working with the VMS1500 and database library 116 the medical researcher can find theheart's constituent profiles and look for the interaction of the weaksignal and chemical imbalance. The medical researcher can trace theeffects of the disease from the quantum level to the cells to the heart,circulatory system to the person and on to the environment. She cancompare the investigated results to the baseline models for normalhealthy profiles. She studies the normal functions of the profiles andbuilds a Profile of the disease and the necessary Requirements neededfor a proficient outcome displayed by the input/output modules (110,112). The various aspects of a profile in a medical simulator areillustrated in FIG. 15.

With the Virtual Medical Simulator 1500, the database library 116, andthe requirements module (110), the medical researcher establishesvariances between normal baseline profiles and the diseased profiles.She determines how the negatives might be rectified and decides onpossible candidates. The medical researcher then sets up her assaytesting utilizing the Chemical bench module 210. Assay is the presenceof substance and amount of the substance that determine the biologicalor pharmacological potency of say a drug, chemical, molecule, etc.

A potential solution (cure) for the abnormality is theoretically workedout by the medical researcher using the VMS 1500, the database library116 and the requirements (or input) module 110. The correctivecompositions of matter candidates are calculated using the QMIP systemmodules shown in FIGS. 1 and 2 that determine the compound propertiesconsidered necessary. The QMIP system defines both the micro and macroproperties desired in the compositions or compounds to be produced whichis reported to the input/output modules (110, 112).

Next, the output candidate data is sent to the tradeoff module 118 forrank ordering. The artificial intelligence module 208 advances thesystem procedures as it learns from the successes and failures ofprevious runs. The virtual medical simulator of FIG. 15 can be includedin the QMIP system of FIG. 1 (for example, being coupled to thedatabase) and programmed and configured for medical research.

Tradeoffs can be done per an efficacy scale that uses the virtualmedical simulator 1500 and the database library 116 to evaluate thecandidates including: intensity of response/individual target andnon-target; dosage requirements/individual target and nontarget;toxicity/individual target and non-target; and target is the specificdisease, cell, living matter, organ, organism, human, animal, plant,synthetic organism, environment, etc. The target is what the system isprogrammed to influence.

Non-target is unspecific disease, cell, living matter, organ, organism,human, animal, plant, synthetic organism, environment, etc. Thenon-targets correlate to the side effects that could involve organismsthat were not being targeted.

QMIP can also be used to enhance performance and condition, such as forthe body to heal or organs to re-grow and or be able to withstand newstresses. We age because we wear out. The physics of entropy determinesthe breakdown of cellular processes in a cell by the accumulated effectsof repetitive stress. Leonard Hayflick, the original discoverer ofcellular aging, believes the ultimate cause of aging is the increasingmolecular disorder, as described in the article “Why Do We Age?,”authored by Brooke Borei in Popular Science (Mar. 30, 2016).

By using the Nosanow Fermion Wave Module 202 and other QMIP Modules,heath quantum researchers can improve our understanding of the agingprocess. Knowing the “why” (perhaps DNA replication), the quantum healthresearchers can find how to prevent and reverse the moleculardecomposition.

Consider the exemplary case where a medical researcher is given a sampleto analyze. The QMIP system can be used to take the sample and give acomplete analysis of the compound/substance anomalies.

In the beginning, the QMIP system and platform will map the drug to thetargeted cell signaling; Drug 1 versus Disease A. The QMIP AI 208determines or predicts and maps Drug 1's effectiveness. The QMIP systemcontinues to evaluate how well Drug 1 performs and records the resultsin the database library 116. The Library grows as Drug 2 isinteractively measured against Disease B and then against Disease A andif there are predicted interactions with Drug 1. Again, QMIP records theresults and builds on the database library module 116. The QMIP systemcan then be programmed and configured to evaluate a drug and calculatethe effectiveness against previous targets and will start refining howthe drug reacts with non-targeted cells, drugs and profiles identifiedabove. The QMIP system prioritizes future designs and develop insightsas the searches and capabilities become wider and deeper intonon-targeted effects.

Better understanding on unintended side effects and predicted reactionsprovide feedback for confirmation/validation testing. It might not beunusual that earlier Drug 1 vs Disease A may find additional sideeffects or find that Drug 369 works a little less effectively againstDisease A but with less side effects. The use of an efficacy scale ortherapeutic index becomes multi-dimensional as specific conditions andgenetics require custom made drug mixtures, delivery systems and dosagesper host. The database library 116 continues to grow and delivers betterinsights. The calculations may grow and special quantum computers(enabled by the QMIP system) may be required to compute with anexponential increase of power.

The QMIP system with the database library 116 evaluates treatmentsversus targeted disease and non-targeted host biological functions toproduce an acceptable health model that speeds time and expense tofinding novel cures that regulatory agencies will approve.

The QMIP system develops a host biological model that can be used tosimulate a disease's effect and drug/treatment response.

The QMIP can monitor the drug's effect during testing/trials to provideinformation on effectiveness, dosage, safety, toxicity, and known andunknown side effects. The Simulation module 206 and VMS 1500 Modules areused to replicate the testing done in a test animal, human trials, andindividual actual usage. The profiles stored in/by the Virtual MedicalSimulator are illustrated in FIG. 15 according to one embodiment.

Quantum Financial Services Example

Table 5 below depicts a process for active portfolio optimizationdeveloped using the QMIP system. Open Banking (where banks give accessto the use of their APIs to others) is causing a transformation in thefinancial services industry. The requirement for dramatic increases ofbig data analysis, management and productization down to the individualmain street customer to maximize opportunities can be more readily metin its entirety by quantum computing and quantum applications and, inparticular, by using the QMIP system. Central to the financialindustries business is the ability to optimize risk and return.Therefore, the process flow diagram of Table 5 focuses on portfoliooptimization. There are indeed many ancillary products (loan offerings),services (budget management) and functions (credit/asset scoring) thatcan be associated with financial services, which quantum computing couldenable or better support. Consequently, the financial services industrywill undoubtedly achieve this transformation by using the QMIP system aspartially represented by the above diagram and described in thefollowing notes.

In this example, at stage 1 in Table 5, the QMIP system can beprogrammed and configured to manage large amounts of data about moresubtle and complex market information, provide greater and fastercomputing power that is able to run tailored and moderately complexalgorithms efficiently, and create Noisy Intermediate Scale Quantum(NISQ) libraries of algorithms specifically programmed and configuredfor optimization of NISQ era computers.

Further, in this example at 3 in Table 5, the QMIP system can beprogrammed and configured to manage large amounts of data andinformation about subtle and complex market drivers of securities,provide greater and faster computing power that run complex optimizationalgorithms efficiently, and create more efficient complex algorithmsthat reflect the market system more completely. In stages 3 and 4 ofTable 5, the QMIP system can be programmed and configured to managelarge amounts of data from growing virtual data sets, provide greaterand faster computing power that runs complex evolving algorithms, andcreate more efficient complex pattern recognition algorithms thatpredict behaviors rapidly.

At stages 5 and 6 of Table 5, the QMIP system can be programmed andconfigured to manage large amounts of data, provide greater and fastercomputing power that run complex timely accurate simulation algorithmsthat capture short to long market period descriptions, and createin-depth complex algorithms that determine the best optimized portfoliowith the least uncertainty.

Quantum and Classical Computing Devices

Various implementations of features described herein can use any type ofsystem and/or service. Any type of electronic device can make use offeatures described herein. Some implementations can provide one or morefeatures described herein on client or server devices disconnected fromor intermittently connected to computer networks.

FIG. 13 is a block diagram of an example quantum computing device 106having one or more Nosanow qubits (1306, 1308) in accordance with someimplementations. In particular, the quantum computing device 106includes an input control program 1032, input control signals 1304, afirst Nosanow qubit 1306 shown interacting (wavy line) with a secondNosanow qubit 1308 with an output signal 1310 sensed and received by ameasurement data output module 1312.

In operation, the classical computing device 104 of the QMIP system canset up the quantum computing device 106 according to set-up parametersdetermined by the QMIP system and control the quantum computing device106 via control signals based on control parameters or informationsupplied by the QMIP system to maximize the time that the Nosanow qubitsare in a coherent state.

FIG. 14 is a block diagram of an example classical (i.e., non-quantum)computing device 1400 which may be used to implement one or morefeatures described herein. In one example, device 1400 may be used toimplement some or all of the QMIP core processing module 102 shown inFIG. 1. Device 1400 can be any suitable computer system, server, orother electronic or hardware device as described herein.

One or more methods described herein (e.g., 300 and/or 1000) can be runin a standalone program that can be executed on any type of computingdevice, a program run on a web browser, a mobile application (“app”) runon a mobile computing device (e.g., cell phone, smart phone, tabletcomputer, wearable device (wristwatch, armband, jewelry, headwear,virtual reality goggles or glasses, augmented reality goggles orglasses, head mounted display, etc.), laptop computer, etc.).

In one example, a client/server architecture can be used, e.g., a mobilecomputing device (as a client device) sends user input data to a serverdevice and receives from the server the final output data for output(e.g., for display). In another example, computations can be performedwithin the mobile app (and/or other apps) on the mobile computingdevice. In another example, computations can be split between the mobilecomputing device and one or more server devices.

In some implementations, device 1400 includes a processor 1402, a memory1404, and I/O interface 1406. Processor 1402 can be one or moreprocessors and/or processing circuits to execute program code andcontrol basic operations of the device 1400. A “processor” includes anysuitable hardware system, mechanism or component that processes data,signals or other information. A processor may include a system with ageneral-purpose central processing unit (CPU) with one or more cores(e.g., in a single-core, dual-core, or multi-core configuration),multiple processing units (e.g., in a multiprocessor configuration), agraphics processing unit (GPU), a field-programmable gate array (FPGA),an application-specific integrated circuit (ASIC), a complexprogrammable logic device (CPLD), dedicated circuitry for achievingfunctionality, a special-purpose processor to implement neural networkmodel-based processing, neural circuits, processors optimized for matrixcomputations (e.g., matrix multiplication), or other systems.

In some implementations, processor 1402 may include one or moreco-processors that implement neural-network processing. In someimplementations, processor 1402 may be a processor that processes datato produce probabilistic output, e.g., the output produced by processor1402 may be imprecise or may be accurate within a range from an expectedoutput. Processing need not be limited to a particular geographiclocation or have temporal limitations. For example, a processor mayperform its functions in “real-time,” “offline,” in a “batch mode,” etc.Portions of processing may be performed at different times and atdifferent locations, by different (or the same) processing systems. Acomputer may be any processor in communication with a memory.

Memory 1404 is typically provided in device 1400 for access by theprocessor 1402 and may be any suitable processor-readable storagemedium, such as random-access memory (RAM), read-only memory (ROM),Electrically Erasable Read-only Memory (EEPROM), Flash memory, etc.,suitable for storing instructions for execution by the processor, andlocated separate from processor 1402 and/or integrated therewith. Memory1404 can store software operating on the server device 1400 by theprocessor 1402, including an operating system 408, machine-learningapplication 1430, QMIP application 1412, and application data 1414.Other applications may include applications such as a data displayengine, web hosting engine, image display engine, notification engine,social networking engine, etc. In some implementations, themachine-learning application 1430 and QMIP application 1412 can eachinclude instructions that enable processor 1402 to perform functionsdescribed herein, e.g., some or all of the methods of FIG. 3-6 or 10.

The machine-learning application 1430 can include one or more NERimplementations for which supervised and/or unsupervised learning can beused. The machine learning models can include multi-task learning basedmodels, residual task bidirectional LSTM (long short-term memory) withconditional random fields, statistical NER, etc. The Device can alsoinclude a QMIP system application 1412 (including core processingmodules) as described herein and other applications. One or more methodsdisclosed herein can operate in several environments and platforms,e.g., as a stand-alone computer program that can run on any type ofcomputing device, as a web application having web pages, as a mobileapplication (“app”) run on a mobile computing device, etc.

In various implementations, machine-learning application 1430 mayutilize Bayesian classifiers, support vector machines, neural networks,or other learning techniques. In some implementations, machine-learningapplication 1430 may include a trained model 1434, an inference engine1436, and data 1432. In some implementations, data 432 may includetraining data, e.g., data used to generate trained model 1434. Forexample, training data may include any type of data suitable fortraining a model for QMIP tasks, such as material properties, desiredoperational characteristics, etc. associated with quantum mechanics asdescribed herein. Training data may be obtained from any source, e.g., adata repository specifically marked for training, data for whichpermission is provided for use as training data for machine-learning,etc. In implementations where one or more users permit use of theirrespective user data to train a machine-learning model, e.g., trainedmodel 1434, training data may include such user data. In implementationswhere users permit use of their respective user data, data 1432 mayinclude permitted data.

In some implementations, data 1432 may include collected data such ascandidate new materials identified along with the input parameters forthose materials. In some implementations, training data may includesynthetic data generated for the purpose of training, such as data thatis not based on user input or activity in the context that is beingtrained, e.g., data generated from simulated conversations,computer-generated images, etc. In some implementations,machine-learning application 1430 excludes data 1432. For example, inthese implementations, the trained model 1434 may be generated, e.g., ona different device, and be provided as part of machine-learningapplication 1430. In various implementations, the trained model 1434 maybe provided as a data file that includes a model structure or form, andassociated weights. Inference engine 1436 may read the data file fortrained model 1434 and implement a neural network with nodeconnectivity, layers, and weights based on the model structure or formspecified in trained model 1434.

Machine-learning application 1430 also includes a trained model 1434. Insome implementations, the trained model 1434 may include one or moremodel forms or structures. For example, model forms or structures caninclude any type of neural-network, such as a linear network, a deepneural network that implements a plurality of layers (e.g., “hiddenlayers” between an input layer and an output layer, with each layerbeing a linear network), a convolutional neural network (e.g., a networkthat splits or partitions input data into multiple parts or tiles,processes each tile separately using one or more neural-network layers,and aggregates the results from the processing of each tile), asequence-to-sequence neural network (e.g., a network that takes as inputsequential data and produces as output a result sequence), etc.

The model form or structure may specify connectivity between variousnodes and organization of nodes into layers. For example, nodes of afirst layer (e.g., input layer) may receive data as input data 1432 orapplication data 1414. Such data can include, for example, images, e.g.,when the trained model is used for one or more QMIP functions describedherein. Subsequent intermediate layers may receive as input output ofnodes of a previous layer per the connectivity specified in the modelform or structure. These layers may also be referred to as hiddenlayers. A final layer (e.g., output layer) produces an output of themachine-learning application. For example, the output may be a set ofcandidate materials, an indication that a candidate material meets theinput criteria, etc. depending on the specific trained model. In someimplementations, model form or structure also specifies a number and/ortype of nodes in each layer.

In different implementations, the trained model 1434 can include aplurality of nodes, arranged into layers per the model structure orform. In some implementations, the nodes may be computational nodes withno memory, e.g., configured to process one unit of input to produce oneunit of output. Computation performed by a node may include, forexample, multiplying each of a plurality of node inputs by a weight,obtaining a weighted sum, and adjusting the weighted sum with a bias orintercept value to produce the node output.

In some implementations, the computation performed by a node may alsoinclude applying a step/activation function to the adjusted weightedsum. In some implementations, the step/activation function may be anonlinear function. In various implementations, such computation mayinclude operations such as matrix multiplication. In someimplementations, computations by the plurality of nodes may be performedin parallel, e.g., using multiple processors cores of a multicoreprocessor, using individual processing units of a GPU, orspecial-purpose neural circuitry. In some implementations, nodes mayinclude memory, e.g., may be able to store and use one or more earlierinputs in processing a subsequent input. For example, nodes with memorymay include long short-term memory (LSTM) nodes. LSTM nodes may use thememory to maintain “state” that permits the node to act like a finitestate machine (FSM). Models with such nodes may be useful in processingsequential data, e.g., words in a sentence or a paragraph, frames in avideo, speech or other audio, etc.

In some implementations, trained model 1434 may include embeddings orweights for individual nodes. For example, a model may be initiated as aplurality of nodes organized into layers as specified by the model formor structure. At initialization, a respective weight may be applied to aconnection between each pair of nodes that are connected per the modelform, e.g., nodes in successive layers of the neural network. Forexample, the respective weights may be randomly assigned, or initializedto default values. The model may then be trained, e.g., using data 1432,to produce a result.

For example, training may include applying supervised learningtechniques. In supervised learning, the training data can include aplurality of inputs (e.g., a set of material properties or requirements)and a corresponding expected output for each input (e.g., one or morecandidate materials). Based on a comparison of the output of the modelwith the expected output, values of the weights are automaticallyadjusted, e.g., in a manner that increases a probability that the modelproduces the expected output when provided similar input.

In some implementations, training may include applying unsupervisedlearning techniques. In unsupervised learning, only input data may beprovided and the model may be trained to differentiate data, e.g., tocluster input data into a plurality of groups, where each group includesinput data that are similar in some manner. For example, the model maybe trained to identify quantum material properties that are associatedwith input properties or criteria for material design.

In another example, a model trained using unsupervised learning maycluster words based on the use of the words in data sources. In someimplementations, unsupervised learning may be used to produce knowledgerepresentations, e.g., that may be used by machine-learning application1430. In various implementations, a trained model includes a set ofweights, or embeddings, corresponding to the model structure. Inimplementations where data 1432 is omitted, machine-learning application1430 may include trained model 1434 that is based on prior training,e.g., by a developer of the machine-learning application 1430, by athird-party, etc. In some implementations, trained model 1434 mayinclude a set of weights that are fixed, e.g., downloaded from a serverthat provides the weights.

Machine-learning application 1430 also includes an inference engine1436. Inference engine 1436 is configured to apply the trained model1434 to data, such as application data 1414, to provide an inference. Insome implementations, inference engine 1436 may include software code tobe executed by processor 1402. In some implementations, inference engine1436 may specify circuit configuration (e.g., for a programmableprocessor, for a field programmable gate array (FPGA), etc.) enablingprocessor 1402 to apply the trained model. In some implementations,inference engine 1436 may include software instructions, hardwareinstructions, or a combination. In some implementations, inferenceengine 1436 may offer an application programming interface (API) thatcan be used by operating system 1408 and/or QMIP application 1412 toinvoke inference engine 1436, e.g., to apply trained model 1434 toapplication data 1414 to generate an inference.

Machine-learning application 1430 may provide several technicaladvantages. For example, when trained model 1434 is generated based onunsupervised learning, trained model 1434 can be applied by inferenceengine 1436 to produce knowledge representations (e.g., numericrepresentations) from input data, e.g., application data 1414. Forexample, a model trained for quantum mechanics tasks may producepredictions and confidences for given input information about a quantummaterial or other quantum computing output. A model trained forsuggesting quantum materials may produce a suggestion for one or morenew materials meeting one or more input criteria. In someimplementations, such representations may be helpful to reduceprocessing cost (e.g., computational cost, memory usage, etc.) togenerate an output (e.g., a suggestion, a prediction, a classification,etc.). In some implementations, such representations may be provided asinput to a different machine-learning application that produces outputfrom the output of inference engine 1436.

In some implementations, knowledge representations generated bymachine-learning application 1430 may be provided to a different devicethat conducts further processing, e.g., over a network. In suchimplementations, providing the knowledge representations rather than theimages may provide a technical benefit, e.g., enable faster datatransmission with reduced cost.

In some implementations, machine-learning application 1430 may beimplemented in an offline manner. In these implementations, trainedmodel 1434 may be generated in a first stage and provided as part ofmachine-learning application 1430. In some implementations,machine-learning application 1430 may be implemented in an onlinemanner. For example, in such implementations, an application thatinvokes machine-learning application 1430 (e.g., operating system 1408,one or more of QMIP application 1412 or other applications) may utilizean inference produced by machine-learning application 1430, e.g.,provide the inference to a user, and may generate system logs (e.g., ifpermitted by the user, an action taken by the user based on theinference; or if utilized as input for further processing, a result ofthe further processing). System logs may be produced periodically, e.g.,hourly, monthly, quarterly, etc. and may be used, with user permission,to update trained model 1434, e.g., to update embeddings for trainedmodel 1434.

In some implementations, machine-learning application 1430 may beimplemented in a manner that can adapt to particular configuration ofdevice 1400 on which the machine-learning application 1430 is executed.For example, machine-learning application 430 may determine acomputational graph that utilizes available computational resources,e.g., processor 1402. For example, if machine-learning application 1430is implemented as a distributed application on multiple devices,machine-learning application 1430 may determine computations to becarried out on individual devices in a manner that optimizescomputation. In another example, machine-learning application 1430 maydetermine that processor 1402 includes a GPU with a particular number ofGPU cores (e.g., 1000) and implement the inference engine accordingly(e.g., as 1000 individual processes or threads).

In some implementations, machine-learning application 1430 may implementan ensemble of trained models. For example, trained model 1434 mayinclude a plurality of trained models that are each applicable to sameinput data. In these implementations, machine-learning application 1430may choose a particular trained model, e.g., based on availablecomputational resources, success rate with prior inferences, etc. Insome implementations, machine-learning application 1430 may executeinference engine 1436 such that a plurality of trained models isapplied. In these implementations, machine-learning application 1430 maycombine outputs from applying individual models, e.g., using avoting-technique that scores individual outputs from applying eachtrained model, or by choosing one or more particular outputs. Further,in these implementations, machine-learning application may apply a timethreshold for applying individual trained models (e.g., 0.5 ms) andutilize only those individual outputs that are available within the timethreshold. Outputs that are not received within the time threshold maynot be utilized, e.g., discarded. For example, such approaches may besuitable when there is a time limit specified while invoking themachine-learning application, e.g., by operating system 1408 or one ormore other applications, e.g., QMIP application 1412.

In different implementations, machine-learning application 1430 canproduce different types of outputs. For example, machine-learningapplication 1430 can provide representations or clusters (e.g., numericrepresentations of input data), labels (e.g., for input data thatincludes images, documents, etc.), phrases or sentences (e.g.,descriptive of an image or video, suitable for use as a response to aninput sentence, suitable for use to determine context during aconversation, etc.), images (e.g., generated by the machine-learningapplication in response to input), audio or video (e.g., in response aninput video, machine-learning application 1430 may produce an outputvideo with a particular effect applied, e.g., rendered in a comic-bookor particular artist's style, when trained model 1434 is trained usingtraining data from the comic book or particular artist, etc. In someimplementations, machine-learning application 1430 may produce an outputbased on a format specified by an invoking application, e.g., operatingsystem 1408 or one or more applications, e.g., QMIP application 1412. Insome implementations, an invoking application may be anothermachine-learning application. For example, such configurations may beused in generative adversarial networks, where an invokingmachine-learning application is trained using output frommachine-learning application 1430 and vice-versa.

Any of software in memory 1404 can alternatively be stored on any othersuitable storage location or computer-readable medium. In addition,memory 1404 (and/or other connected storage device(s)) can store one ormore messages, one or more taxonomies, electronic encyclopedia,dictionaries, thesauruses, knowledge bases, message data, grammars, userpreferences, and/or other instructions and data used in the featuresdescribed herein. Memory 1404 and any other type of storage (magneticdisk, optical disk, magnetic tape, or other tangible media) can beconsidered “storage” or “storage devices.”

I/O interface 1406 can provide functions to enable interfacing theserver device 1400 with other systems and devices. Interfaced devicescan be included as part of the device 400 or can be separate andcommunicate with the device 1400. For example, network communicationdevices, storage devices (e.g., memory and/or database 106), andinput/output devices can communicate via I/O interface 1406. In someimplementations, the I/O interface can connect to interface devices suchas input devices (keyboard, pointing device, touchscreen, microphone,camera, scanner, sensors, etc.) and/or output devices (display devices,speaker devices, printers, motors, etc.).

Some examples of interfaced devices that can connect to I/O interface1406 can include one or more display devices 1420 and one or more datastores 1438 (as discussed above). The display devices 1420 that can beused to display content, e.g., a user interface of an output applicationas described herein. Display device 1420 can be connected to device 400via local connections (e.g., display bus) and/or via networkedconnections and can be any suitable display device. Display device 1420can include any suitable display device such as an LCD, LED, or plasmadisplay screen, CRT, television, monitor, touchscreen, 3-D displayscreen, or other visual display device. For example, display device 1420can be a flat display screen provided on a mobile device, multipledisplay screens provided in a goggles or headset device, or a monitorscreen for a computer device.

The I/O interface 1406 can interface to other input and output devices.Some examples include one or more cameras which can capture images. Someimplementations can provide a microphone for capturing sound (e.g., as apart of captured images, voice commands, etc.), audio speaker devicesfor outputting sound, or other input and output devices.

For ease of illustration, FIG. 14 shows one block for each of processor1402, memory 1404, I/O interface 1406, and software blocks 1408, 1412,and 1430. These blocks may represent one or more processors orprocessing circuitries, operating systems, memories, I/O interfaces,applications, and/or software modules. In other implementations, device1400 may not have all of the components shown and/or may have otherelements including other types of elements instead of, or in additionto, those shown herein. While some components are described asperforming blocks and operations as described in some implementationsherein, any suitable component or combination of components ofenvironment 100, device 1400, similar systems, or any suitable processoror processors associated with such a system, may perform the blocks andoperations described.

In some implementations, the QMIP system could include amachine-learning model (as described herein) for tuning the system(e.g., selecting candidate materials and corresponding thresholds) topotentially provide improved accuracy. Inputs to the machine learningmodel can include material properties and/or a descriptor vector thatdescribes materials and includes information about material properties.Example machine-learning model input can include target materialproperties or characteristics for a simple implementation and can beaugmented with descriptor vector features for a more advancedimplementation. Output of the machine-learning module can include aprediction of one or more candidate materials.

One or more methods described herein (e.g., method 300 or 1000) can beimplemented by computer program instructions or code, which can beexecuted on a computer. For example, the code can be implemented by oneor more digital processors (e.g., microprocessors or other processingcircuitry), and can be stored on a computer program product including anon-transitory computer readable medium (e.g., storage medium), e.g., amagnetic, optical, electromagnetic, or semiconductor storage medium,including semiconductor or solid state memory, magnetic tape, aremovable computer diskette, a random access memory (RAM), a read-onlymemory (ROM), flash memory, a rigid magnetic disk, an optical disk, asolid-state memory drive, etc. The program instructions can also becontained in, and provided as, an electronic signal, for example in theform of software as a service (SaaS) delivered from a server (e.g., adistributed system and/or a cloud computing system). Alternatively, oneor more methods can be implemented in hardware (logic gates, etc.), orin a combination of hardware and software. Example hardware can beprogrammable processors (e.g., Field-Programmable Gate Array (FPGA),Complex Programmable Logic Device), general purpose processors, graphicsprocessors, Application Specific Integrated Circuits (ASICs), and thelike. One or more methods can be performed as part of or component of anapplication running on the system, or as an application or softwarerunning in conjunction with other applications and operating system.

One or more methods described herein can be run in a standalone programthat can be run on any type of computing device, a program run on a webbrowser, a mobile application (“app”) run on a mobile computing device(e.g., cell phone, smart phone, tablet computer, wearable device(wristwatch, armband, jewelry, headwear, goggles, glasses, etc.), laptopcomputer, etc.). In one example, a client/server architecture can beused, e.g., a mobile computing device (as a client device) sends userinput data to a server device and receives from the server the finaloutput data for output (e.g., for display). In another example,computations can be performed within the mobile app (and/or other apps)on the mobile computing device. In another example, computations can besplit between the mobile computing device and one or more serverdevices.

Although the description has been described with respect to particularimplementations thereof, these particular implementations are merelyillustrative, and not restrictive. Concepts illustrated in the examplesmay be applied to other examples and implementations.

Note that the functional blocks, operations, features, methods, devices,and systems described in the present disclosure may be integrated ordivided into different combinations of systems, devices, and functionalblocks. Any suitable programming language and programming techniques maybe used to implement the routines of particular implementations.Different programming techniques may be employed, e.g., procedural orobject-oriented. The routines may execute on a single processing deviceor multiple processors. Although the steps, operations, or computationsmay be presented in a specific order, the order may be changed indifferent particular implementations. In some implementations, multiplesteps or operations shown as sequential in this specification may beperformed at the same time.

Nosanow Equation Theory

The Nosanow Equation Theory can include a method that can be used tocalculate and predict the properties of a Fermion and Fermion System aswell as a Boson and Boson System, an application being a newsuperconductor(s) (accounts for the Macroscopic Phase Discontinuity).Some implementations can include application that demonstrates thatspin-orbit coupling does not exist, but rather another nuclear force.

The Nosanow Equation Theory can explain new particles such as:

A new Quasi Particle defined as a QUASON1 (which are the ElementaryExcitations of a Grand Fermi Quason Field), and which appears as theQUANTA of the Grand Free Quason Fermi Field.

A new Quasi Particle defined as a QUASON2 (which are the ElementaryExcitations of a Grand Boson Quason Field), and which appears as theQUANTA of the Grand Free Quason Boson Field.

A new Non-Relativistic Positron particle, as described above.

A new Non-Relativistic Boson particle, as described above.

The Nosanow Equation Theory can also provide:

Recognition that one can essentially add the potential energy to thekinetic energy

Ĥ_(CF) = [Ĥ_(F) + Ĥ_(GP)] + [Ĥ_(a) − Ĥ_(GP)].

Recognition that Ĥ_(GP) is an order of magnitude smaller than Ĥ_(α) buthas the same order of magnitude as Ĥ_(F). Consequently, [Ĥ_(α)−Ĥ_(GP)]≅but Ĥ_(GP) has a significant effect on Ĥ_(F).

Recognition that [Ĥ_(F)+Ĥ_(GP)] has the same form as the BCS (Bardeen,Cooper and Schrieffer) Model Hamiltonian. This insight is fundamental toa new superconductor theory which can be applied to create new, improvedsuperconductors.

The analytical determination of helium-3's ground state, criticaltemperature and of its thermodynamic properties.

The ability to predict new superconductors and super fluids using aninnovative scheme that simplifies the computations and reducessubstantially the numerical process time. This also applies toparticles, atoms, molecules, compounds, alloys and composites alike.

A spin orbit coupling force does not exist, which can now be verified bythe Nosanow Fermion Wave Equation.

A different nuclear force actually exists, as predicted by the NosanowFermion Wave Equation, which now replaces the current spin orbitcoupling force that was an extrapolation.

A new Nosanow application of the BCS Unitary Transformation and thecomprehension of the implications.

Ĥ_(ā) is an educated guess that may be written to introduce a morecomplex spin as represented by the terms of η. The Nosanow Fermion WaveEquation is written with spin as an integral part, unlike theSchrodinger Wave Equation where spin must be added separately.

A relativistic method can be used as a guide to write a nonrelativisticexpression for [Ĥ_(F)+Ĥ_(GP)].

${\hat{H}}_{GP} = {\frac{1}{V}\Sigma_{k_{1}}\Sigma_{k_{2}}\langle {{\overset{arrow}{k}}_{1}{{v( r_{1,2} )}}{\overset{arrow}{k}}_{2}} \rangle\mspace{14mu}{is}\mspace{14mu} a\mspace{14mu}{totally}\mspace{14mu}{new}\mspace{14mu}{and}\mspace{14mu}{solvable}\mspace{14mu}{{equation}.}}$

This [Ĥ_(F)+Ĥ_(GP)] Hamiltonian yields a new and solvable wave equation:

${i\;\hslash\frac{\partial}{\partial t}{\psi( {\{ {\overset{arrow}{r},\overset{arrow}{S}} \};t} )}I_{4}}\overset{\sim}{=}{\pm {{{\hat{H}}_{Q}( {\{ \overset{arrow}{p} \}_{N};\mu;\Delta_{F}} )}\lbrack {{\psi( {\{ {\overset{arrow}{r},\overset{arrow}{S}} \};t} )}I_{4}} \rbrack}}$

This Ĥ_(α)≅Ĥ _(α) Hamiltonian yields a new interaction expression Ĥ _(α)(μ; Δ≡

${{\frac{1}{2V}{\Sigma\eta}_{1}{\cdots\Sigma}_{\eta_{4}}{\int_{V}{d{\overset{arrow}{r}}_{1}{\int_{V}{d{\overset{arrow}{r}}_{2}{\psi^{\dagger}( {{\overset{arrow}{r}}_{1},\eta_{1}} )}{\psi^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}}}}}} < \eta_{1}},{\eta_{2}{{V_{\overset{\_}{a}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},{\eta_{3} > {{\hat{\psi}( {{\overset{arrow}{r}}_{2},\eta_{4}} )}{\hat{\psi}( {{\overset{arrow}{r}}_{1},\eta_{3}} )}}}$

A new quantum mechanics identity, known as the Nosanow Identity, hasbeen constructed using the Energy of a Free Quason: E_(p)²(μ;Δ)I₄=[∈_(P) ²−2μ∈_(p)+μ²+Δ²]I₄; I₄ is a unit matrix of rank 4.

A new and solvable Nosanow Wave Equation for a Fermion System has beencreated now known as the Nosanow Fermion Wave Equation. This waveequation is more comprehensive, accurate, and solvable than anything inexistence today.

${\{ {{\pm {D( {\overset{arrow}{r},\overset{arrow}{S}} )}} + {\frac{1}{V}\Sigma_{\eta_{1}}\Sigma_{\eta_{2}}\Sigma_{\eta_{4}}{\int_{V}{d{\overset{arrow}{r}}_{2}\langle {\eta_{1},{\eta_{2}{{V_{\overset{\_}{a}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},\eta_{3}} \rangle \times {\psi^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}{\psi( {{\overset{arrow}{r}}_{2},\eta_{4}} )}}}}} \}{\psi( {\overset{arrow}{r},\overset{arrow}{s}} )}} = 0$

A new solvable Nosanow Wave Equation for a Boson System has been creatednow known as the Nosanow Boson Wave Equation. This wave equation is morecomprehensive, accurate, and solvable than anything in existence today.

The Nosanow Fermion and Nosanow Boson Wave Equations calculate QuantumMechanical properties that are then transformed into thermodynamicpermutations at the macroscopic level.

The Nosanow Fermion Wave Equation generalized in the ElectromagneticField.

The Nosanow Boson Wave Equation generalized in the ElectromagneticField.

The physics of the Nuclear Shell Model can now be explained andaccurately represented mathematically.

The Nuclear Shell Model in turn now validates the Nosanow Fermion andNosanow Boson Wave Equations.

The analytical determination of helium-3's ground state, criticaltemperature and of its thermodynamic properties validates the NosanowFermion and Nosanow Boson Wave Equations.

A revised and more comprehensive periodic table can now be developedbased upon nucleon interactions.

The use of 4×4 matrices in the development of the Nosanow Fermion WaveEquation can be expanded to explore and express new insights intoquantum mechanics. In addition to the 4×4 matrices, a 16×16 matrix isalso developed and used in the Nosanow Fermion Wave Equation andHamiltonian.

The actual AI module, due to the expansion of new knowledge that can nowbe provided by the Nosanow Fermion and Nosanow Boson Wave Equations, isable to provide more subtle and discerning procedures for establishing,maintaining and manipulating quantum bits.

Example applications can include a new periodic table built on nucleoninteractions, a new branch of Computational Chemistry based on nucleoninteractions, an expansion of the current superconductor theory andpractice, a valid and predictive method that can express how quantummechanics can explain classical mechanics, an expansion of the currentunderstandings of Quantum Mechanics and the ability to apply its use, anability to solve problems that the Schrodinger Equation can solve and,most notably, cannot solve, an ability to explain electron tunnelingmore completely, the prediction that a new Non-Relativistic Positronparticle exists, the prediction that a new Non-Relativistic Bosonparticle exists, and an ability to correct and explain a more accurateNuclear Shell Model Theory.

Nosanow Fermion Wave Equation Primer

This section describes the development and construction of new NosanowFermion Wave Equations. There are many different Fermion Systems whoseHamiltonians have the form:

Ĥ_(CF) = Ĥ_(F) + Ĥ_(α),

where Ĥ_(F) designates the Hamiltonian of N Free Fermions and Ĥ_(α)denotes one of many possible interaction Hamiltonians which can beobtained from these Fermion Systems.

BCS Theory Yields:

a superfluid Fermi gas for T≤T_(c) and

an ideal Fermi gas for T>Tc, where T designates the temperature inenergy units and T_(c) denotes the critical temperature. This resultsuggests there is something unusual going on in Fermion systems with asufficient attraction such that Cooper Pairs may exist in the system.This observation is the starting point for the present study.

It is manifest that Ĥ_(CF) contains a Sub-Hamiltonian which hasessentially the same form as the BCS phenomenological Hamiltonian. ThisSub-Hamiltonian may be written:

Ĥ_(SH) ≡ Ĥ_(F) + Ĥ_(GP),

where, in essence, Ĥ_(GP) has the same form as the BCS Grand PairingInteraction term. The Hamiltonian Ĥ_(CF) may therefore be written:

Ĥ_(CG) = [Ĥ_(F) + Ĥ_(GP)] + [Ĥ_(α) − Ĥ_(GP)]

In this expression, Ĥ_(GP) may be neglected relative Ĥ_(α) to because itis a factor of N smaller than Ĥ_(α).

It follows that the Hamiltonian, Ĥ_(Free)≡Ĥ_(F)+Ĥ_(GP), may bediagonalized using the BCS unitary transformation. This Hamiltonianyields the first part of a new Nosanow Fermion Wave Equation. ThisHamiltonian may be written as the equation:

${{i\;\hslash\frac{\partial}{\partial t}{\hat{\psi}( {\{ {\overset{arrow}{r},\overset{arrow}{S}} \};t} )}I_{4}}\overset{\sim}{=}{{\pm {{\hat{H}}_{Free}( {\{ \hat{p} \}_{N};\mu;\Delta} )}}\mspace{11mu}\hat{\psi}\mspace{11mu}( {\{ {\overset{arrow}{r},\overset{arrow}{S}} \};t} )I_{4}}},{where}$Ĥ_(Free)({p̂}_(N); μ; Δ_(F)) ≡ β[ ∈ (p̂) − ɛ(u; Δ_(F))] + (α ⋅ p̂)K(μ; Δ)

and Δ denotes the order parameter for {circumflex over (p)}=p_(F).

It is straightforward to show that the solution of Ĥ_(F)+Ĥ_(GP) may bewritten:

E_(FG)(p) ≡ [ɛ²(p) − 2μɛ(p) + μ² + Δ²(p)]^(1/2).

The quantity Δ(p) is given by an equation which is analogous to the BCSgap equation. In contradistinction to BCS theory, this equation cannotbe used to calculate Δ(p). However, it can be shown that Δ(p)≅Δ(p_(F)),where p_(F) ²/m denotes the Fermi energy. The quantity Δ(p_(F)) maysimply be written Δ.

Furthermore, it is also straightforward to show that the DIRAC approachmay be utilized to construct the Nosanow Wave Equation of this Fermionsystem. In the expression for

E_(FG)(p); ϵ(p̂) ≡ p 2/2m, ɛ(μ; Δ_(F)) ≡ (μ² + Δ_(F)²)^(1/2)  and  K²(μ; Δ_(F)) ≡ ɛ(μ; Δ_(F)) − μ.

It remains to complete the new platform system by adding Ĥ_(α). Theexplicit expression for Ĥ_(Free) may be constructed using Dirac Matricesβ and {right arrow over (α)}. The definitions of these Rank 4 matricesare:

${\beta \equiv {\begin{bmatrix}I & 0 \\0 & {- I}\end{bmatrix}\mspace{14mu}{and}\mspace{14mu}\overset{arrow}{\alpha}} \equiv \begin{bmatrix}0 & \overset{arrow}{\sigma} \\\overset{arrow}{\sigma} & 0\end{bmatrix}},$

where I, {right arrow over (σ)}, and 0 denote matrices of Rank 2. Thesematrices may be used as a guide to construct the relativistic identity:

${{E^{2}(p)}I_{4}} = {{( {{c^{2}p^{2}} + {m^{2}c^{4}}} )I_{4}} \equiv {\lbrack {{c( {\overset{arrow}{\alpha} \cdot \overset{arrow}{p}} )} + {\beta\;{mc}^{2}}} \rbrack^{2}.}}$

(Note: this identity is relativistic invariant.) This identity may begeneralized to construct the nonrelativistic identity:

${{E_{FG}(p)}I_{4}} \equiv {\lbrack {\in^{2}{( \overset{arrow}{p} ) - {2\mu}} \in {( \overset{arrow}{p} ) + \mu^{2} + \Delta^{2}}} \rbrack I_{4}} \equiv {\{ {{\beta\lbrack {( \frac{p^{2}}{2m} ) - {ɛ( {\mu;{\Delta( p_{F} )}} )}} \rbrack} + {( {\overset{arrow}{\alpha} \cdot \overset{arrow}{p}} ){K( {\mu;{\Delta(p)}} )}}} \rbrack^{2}.}$

The proof of this identity follows from the three properties:

1) the cross terms vanish because β and (α·{right arrow over (p)})anti-commute,

2) β²=I₄, and

3) ({right arrow over (α)}·{right arrow over (p)})=p²I₄.

Therefore, the square root of Quadratic Function of p² may be written asa Matrix of Rank 4 whose elements are Quadratic Functions of {rightarrow over (p)}. It follows that

${{E_{FG}(p)}I_{4}} \equiv {\pm \{ {{\beta\lbrack {\in {( \overset{arrow}{p} ) - {ɛ( {\mu;{\Delta( p_{F} )}} )}}} \rbrack} + {( {\overset{arrow}{\alpha} \cdot \overset{arrow}{p}} ){K( {\mu;{\Delta(p)}} )}}} \}}$

where

${\epsilon( \overset{arrow}{p} )} \equiv \frac{p^{2}}{2m}$

designates the Kinetic Energy of One Free Particle.

To process further, the following substitution for E_(FG)(p) and {rightarrow over (p)} may be introduced:

$ {E_{FG}(p)}arrow {i\;\hslash\frac{\partial}{\partial t}\mspace{14mu}{and}\mspace{14mu}\overset{arrow}{p}}arrow{\hat{p}.}  $

Using these substitutions in the expression for E_(FG)(p) yields theFIELD OPERATOR:

${{i\;\hslash\frac{\partial}{\partial t}{\hat{\psi}( {\overset{arrow}{r},{\overset{arrow}{S};t}} )}}\overset{\sim}{=}{{\pm {{\hat{H}}_{Free}( {\hat{p};\mu;{\Delta( p_{F} )}} )}}\mspace{11mu}\hat{\psi}\mspace{11mu}( {\overset{arrow}{r},{\overset{arrow}{S};t}} )}},{where}$${{\hat{H}}_{Free}\{ {{\hat{p};\mu},{\Delta( p_{F} )}} \}} \equiv {{\beta\lbrack {\in {( \hat{p} ) - {ɛ( {u;{\Delta( p_{F} )}} )}}} \rbrack} + {( {\overset{arrow}{\alpha} \cdot \overset{arrow}{p}} ){{k( {\mu;{\Delta(p)}} )}.}}}$

The quantity {right arrow over (S)} denotes the Spin Matrix S≡½ h{rightarrow over (σ)} where {right arrow over (σ)} denotes the Pauli SpinVector whose components are the Pauli Spin Matrices. The Equation forψ({right arrow over (r)},{right arrow over (S)};t) will be called theone particle wave equation.

It is important to observe that Ĥ_(Free)({circumflex over (p)}; μ;Δ)contains both the Dirac and Schrodinger One-Particle Kinetic Energies,({right arrow over (α)}·{right arrow over (p)}) and ∈({acute over (p)}).This result can occur in a nonrelativistic system. Therefore, the waveequation of one quasi particle is strictly a non-relativistic equation.it is straightforward to generalize the one-fermion wave equation to awave equation for a system of N Fermions. The result is:

${{i\;\hslash\frac{\partial}{\partial t}\mspace{11mu}\hat{\psi}\mspace{11mu}( {\{ {\overset{arrow}{r},\overset{arrow}{S}} \}_{N};t} )I_{4}}\overset{\sim}{=}{{\pm {{\hat{H}}_{Free}( {\{ \hat{p} \}_{N};\mu;\Delta} )}}{\hat{\psi}( {\{ {\overset{arrow}{r},\overset{arrow}{S}} \}_{N};t} )}I_{4}}},{where}${p̂}_(N) ≡ {p̂₁; p̂₂; …  ; p̂_(N)}  and$\{ {\overset{arrow}{r},\overset{arrow}{S}} \}_{N} \equiv {\{ {{\overset{arrow}{r}}_{1},{{\overset{arrow}{S}}_{1};{\overset{arrow}{r}}_{2}},{{\overset{arrow}{S}}_{2};\ldots\mspace{14mu};{\overset{arrow}{r}}_{N}},{\overset{arrow}{S}}_{N}} \}.}$

This result completes the analysis of Ĥ_(Free).

Using the notation of Greiner and Reinhardt², the expression for theHamiltonian of One Free Fermion may be written:

${{{\hat{H}}_{Free}( {\overset{arrow}{p},\overset{arrow}{S}} )} \equiv {\sum_{\sigma}{\int_{V}{d\overset{arrow}{\; r\;}{{{\hat{\psi}}^{t}( {\overset{arrow}{r},\sigma} )}\lbrack {{\hat{D}( {\overset{arrow}{r},\sigma} )}{\hat{\psi}( {\overset{arrow}{r},\sigma} )}} \rbrack}}}}},$

where {right arrow over (S)} contains the spin σ,

${{\hat{D}( {\overset{arrow}{r},\sigma} )} \equiv {{\lbrack {{- \frac{\hslash^{2}}{2m}}{{\overset{arrow}{\nabla}}^{2}{- \mu}}} \rbrack I_{4}} \pm {\phi( {\overset{arrow}{r},\sigma} )}}},{{\hat{\phi}( {\overset{arrow}{r},\sigma} )} \equiv {{\beta\lbrack {\in {( \overset{arrow}{p} ) - {ɛ( {\mu;\Delta} )}}} \rbrack} + {( {\overset{arrow}{\alpha} \cdot \overset{arrow}{p}} ){K( {\mu;\Delta} )}}}},$

and Δ denotes the ORDER PARAMETER for {circumflex over (p)}=p_(F).

^([Note 2]) W. Greiner and J. Reinhardt; Field Quantization; SpringerVerlag; Berlin, Heidelberg, N.Y. (1993).

It follows that the Hamiltonian for N Free Fermions may be written:

${{\hat{H}}_{{Free},N}( \{ {\hat{p},\overset{arrow}{S}} \}_{N} )} \equiv {\Sigma_{\sigma_{1}}{\cdots\Sigma}_{\sigma_{N}}{\int_{V}{d{{{\overset{arrow}{r}}_{N}\lbrack {\prod\limits_{i = 1}^{N}\;\{ {{{\hat{\psi}}^{\dagger}( {{\overset{arrow}{r}}_{i},\sigma_{i}} )}{\hat{D}( {{\overset{arrow}{r}}_{i},\sigma_{i}} )}{\hat{\psi}( {{\overset{arrow}{r}}_{i},\sigma_{i}} )}} \}} \rbrack}.}}}}$

The time dependence of the Field Operators is not written explicitly.This completes the construction of the Hamiltonian for N Free Fermions.

The next step is the analysis of the interaction Hamiltonian, thedefinition of this Hamiltonian is:

${{\hat{H}}_{\alpha} \equiv {\frac{1}{2V}\Sigma_{\sigma_{1}}\Sigma_{\sigma_{2}}{\int_{V}{d{\overset{arrow}{r}}_{1}{\int_{V}{d{\overset{arrow}{r}}_{2}{{\hat{\psi}}^{\dagger}( {{\overset{arrow}{r}}_{1},\sigma_{1}} )}{{\hat{\psi}}^{\dagger}( {{\overset{arrow}{r}}_{2},\sigma_{2}} )} \times {v_{\alpha}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}{\hat{\psi}( {{\overset{arrow}{r}}_{2},\sigma_{2}} )}{\hat{\psi}( {{\overset{arrow}{r}}_{1},\sigma_{1}} )}}}}}}},$

the time dependence of the field operators {circumflex over (ψ)}({rightarrow over (r)}, σ) is not written explicitly. It remains to constructthe educated guess for this interaction Hamiltonian.

To carry out this construction, it is necessary to replace the followingquantities,

$\sigma,{{\hat{\psi}( {\overset{arrow}{r},\sigma} )}\mspace{14mu}{and}\mspace{14mu}{v_{\alpha}( {{\overset{arrow}{r}}_{a},{\overset{arrow}{r}}_{b}} )}},$

by new quantities; these quantities will be chosen for intuitive reasonswhich express the fundamental properties of the physical system.

The replacements which will be chosen are:

-   -   1. σ⇒η, where η takes on 4 values,    -   2. ψ(r, σ)⇒        η₁, η₂|V _(α) ({right arrow over (r)}_(α), {right arrow over        (r)}_(b))|η₄, η₃

In the replacement for v_(α)({right arrow over (r)}_(a), {right arrowover (r)}_(b)), the quantity, V _(α) ({right arrow over (r)}_(α), {rightarrow over (r)}_(b)) becomes: V _(α) ({right arrow over (r)}_(a), {rightarrow over (r)}_(b))≡(β⊗β)v _(α) ({right arrow over (r)}_(α), {rightarrow over (r)}_(b))(β⊗β), where (β⊗β) designates the outer product oftwo β-matrices. The quantity (β⊗β) may be written as a matrix of rank16. Using these replacements, the educated guess for the interactionHamiltonian Ĥ _(α) may be written:

${{{\hat{H}}_{\overset{\_}{\alpha}}( {\mu;\Delta} )} \equiv {\frac{1}{2V}\Sigma_{\eta_{1}}{\cdots\Sigma}_{\eta_{4}}{\int_{V}{d{\overset{arrow}{r}}_{1}{\int_{V}{d{\overset{arrow}{r}}_{2}{\psi^{\dagger}( {{\overset{arrow}{r}}_{1},\eta_{1}} )}{\psi^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}}}}}} < \eta_{1}},{\eta_{2}{{V_{\overset{\_}{\alpha}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},{\eta_{3} > {{\hat{\psi}( {{\overset{arrow}{r}}_{2},\eta_{4}} )}{{\hat{\psi}( {{\overset{arrow}{r}}_{1},\eta_{3}} )}.}}}$

This completes the construction of the Hamiltonian for a System of NInteracting Fermions:

${i\;\hslash\frac{\partial}{\partial t}{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )}} = {\lbrack {{\pm {{\hat{H}}_{Free}( {\hat{p};\mu;\Delta} )}} + {{\hat{H}}_{\overset{\_}{\alpha}}( {\mu;\Delta} )}} \rbrack{{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )}.}}$

It remains to construct the Heisenberg equation of motion for the fieldoperator. The explicit expression for this equation may be written:

${i\;\hslash\frac{\partial}{\partial t}{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )}} = {\lbrack {{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )},{\hat{H}}_{CF}} \rbrack \equiv {\lbrack {{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )},{{\pm {\hat{H}}_{Free}} + {\hat{H}}_{\overset{\_}{\alpha}}}} \rbrack.}}$

This equation yields the time evolution of the field operator in termsof the commutator of the field operator and the Hamiltonian. It isstraight forward to show that:

$\lbrack {{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{s}} )},{\pm {\hat{H}}_{Free}}} \rbrack = {{\pm {\hat{D}( {\overset{arrow}{r},\overset{arrow}{s}} )}}{{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{s}} )}.}}$

Further

$\lbrack {{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{s}} )},{\hat{H}}_{\overset{\_}{\alpha}}} \rbrack = {\frac{1}{V}{\sum\limits_{\eta_{1}}{\sum\limits_{\eta_{2}}{\sum\limits_{\eta_{4}}{\int\limits_{V}{d{\overset{arrow}{r}}_{2}\langle {\eta_{1},{\eta_{2}{{V_{\overset{\_}{\alpha}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},\eta_{3}} \rangle \times {{\hat{\psi}}^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}{\hat{\psi}( {{\overset{arrow}{r}}_{2},\eta_{4}} )}{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{s}} )}}}}}}}$

It follows that the Heisenberg equation of motion for {circumflex over(ψ)}({right arrow over (r)}, {right arrow over (s)}) becomes:

${i\;\hslash\frac{\partial}{\partial t}{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{S}} )}} = \{ {{\pm {D( {\overset{arrow}{r},\overset{arrow}{s}} )}} + {\frac{1}{V}{\sum\limits_{\eta_{1}}{\sum\limits_{\eta_{2}}{\sum\limits_{\eta_{4}}{\int\limits_{V}{d{\overset{arrow}{r}}_{2}\langle {\eta_{1},{\eta_{2}{{V_{\overset{\_}{\alpha}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},\eta_{3}} \rangle \times {{\hat{\psi}}^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}{\hat{\psi}( {{\overset{arrow}{r}}_{2},\eta_{4}} )}{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{s}} )}}}}}}}} $

This equation may also be written in a time-independent form:

$\{ {{{\pm {D( {\overset{arrow}{r},\overset{arrow}{s}} )}} + {\frac{1}{V}\Sigma_{\eta_{1}}\Sigma_{\eta_{2}}\Sigma_{\eta_{4}}{\int_{C}{d{\overset{arrow}{r}}_{2}\langle {\eta_{1},{\eta_{2}{{V_{\overset{\_}{\alpha}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},\eta_{3}} \rangle \times {{\hat{\psi}}^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}{\hat{\psi}( {{\overset{arrow}{r}}_{2},\eta_{4}} )}{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{s}} )}}}}} = 0.} $

This time-independent equation is called the Nosanow Fermion WaveEquation. This can also be written using the functions D({right arrowover (r)}, {right arrow over (s)}) and ψ({right arrow over (r)}, {rightarrow over (s)}):

$\{ {{{\pm {D( {\overset{arrow}{r},\overset{arrow}{s}} )}} + {\frac{1}{V}\Sigma_{\eta_{1}}\Sigma_{\eta_{2}}\Sigma_{\eta_{4}}{\int_{C}{d{\overset{arrow}{r}}_{2}\langle {\eta_{1},{\eta_{2}{{V_{\overset{\_}{\alpha}}( {{\overset{arrow}{r}}_{1},{\overset{arrow}{r}}_{2}} )}}\eta_{4}},\eta_{3}} \rangle \times {{\hat{\psi}}^{\dagger}( {{\overset{arrow}{r}}_{2},\eta_{2}} )}{\hat{\psi}( {{\overset{arrow}{r}}_{2},\eta_{4}} )}{\hat{\psi}( {\overset{arrow}{r},\overset{arrow}{s}} )}}}}} = 0.} $

An equation for Boson and Boson Systems is obtained using similartechniques as outlined above with modifications as required by thesupporting particle physics.

The systems, methods and techniques as illustrated in the figuresinclude one or more computer processors capable of accessing stored dataand instructions to perform various steps and may operate in conjunctionwith software modules described herein in order to perform variousfunctions. Many processors may be suitable and will be further describedbelow. The described engines, generators, modules and other componentsmay be or include software modules that are executed by the processor toperform their stated functions. Although the software modules are shownas discrete components, they may be integrated in various ways inaccordance with embodiments of the disclosed subject matter.

The components shown in the figures above may be, include, or beimplemented by a computer or multiple computers. The system of thedisclosed subject matter or portions of the system of the disclosedsubject matter may be in the form of a “processing machine,” i.e., atangibly embodied machine, such as a general-purpose computer or aspecial purpose computer or processor or microprocessor, for example. Asused herein, the term “processing machine” is to be understood toinclude at least one processor that uses at least one memory. The atleast one memory stores a set of instructions. The instructions may beeither permanently or temporarily stored in the memory or memories ofthe processing machine. The processor executes the instructions that arestored in the memory or memories in order to process data. The set ofinstructions may include various instructions that perform a particulartask or tasks, such as any of the processing as described herein. Such aset of instructions for performing a particular task may becharacterized as a program, software program, or simply software.

As noted above, the processing machine, which may be constituted, forexample, by the particular system and/or systems described above,executes the instructions that are stored in the memory or memories toprocess data. This processing of data may be in response to commands bya user or users of the processing machine, in response to previousprocessing, in response to a request by another processing machineand/or any other input, for example. As noted above, the processingmachine used to implement the disclosed subject matter may be ageneral-purpose computer or as described above in many embodiments, maybe implemented as a quantum computer. However, the processing machinedescribed above may also utilize (or be in the form of) any of a widevariety of other technologies including a special purpose computer, acomputer system including a microcomputer, mini-computer or mainframe,for example, a programmed microprocessor, a micro-controller, aperipheral integrated circuit element, a CISC (Customer SpecificIntegrated Circuit) or ASIC (Application Specific Integrated Circuit) orother integrated circuit, a logic circuit, a digital signal processor, aprogrammable logic device such as a FPGA, PLD, PLA or PAL, or any otherdevice or arrangement of devices that is capable of implementing thesteps of the processes of the disclosed subject matter, including aquantum computer.

The processing machine used to implement the disclosed subject mattermay utilize a suitable operating system. Thus, embodiments of thedisclosed subject matter may include a processing machine running theMicrosoft Windows™ Vista™ operating system, the Microsoft Windows™ XP™operating system, the Microsoft Windows™ NT™ operating system, theWindows™ 2000 operating system, the Unix operating system, the Linuxoperating system, the Xenix operating system, the IBM AIX™ operatingsystem, the Hewlett-Packard UX™ operating system, the Novell Netware™operating system, the Sun Microsystems Solaris™ operating system, theOS/2™ operating system, the BeOS™ operating system, the Macintoshoperating system, the Apache operating system, an OpenStep™ operatingsystem or another operating system or platform. It is appreciated thatin order to practice the method of the disclosed subject matter asdescribed above, it is not necessary that the processors and/or thememories of the processing machine be physically located in the samegeographical place. That is, each of the processors and the memoriesused by the processing machine may be located in geographically distinctlocations and connected so as to communicate in any suitable manner.Additionally, it is appreciated that each of the processor and/or thememory may be composed of different physical pieces of equipment.Accordingly, it is not necessary that the processor be one single pieceof equipment in one location and that the memory be another single pieceof equipment in another location. That is, it is contemplated that theprocessor may be two pieces of equipment in two different physicallocations. The two distinct pieces of equipment may be connected in anysuitable manner. Additionally, the memory may include two or moreportions of memory in two or more physical locations.

To explain further, processing as described above is performed byvarious components and various memories. However, it is appreciated thatthe processing performed by two distinct components as described abovemay, in accordance with a further embodiment of the invention, beperformed by a single component. Further, the processing performed byone distinct component as described above may be performed by twodistinct components. In a similar manner, the memory storage performedby two distinct memory portions as described above may, in accordancewith a further embodiment of the invention, be performed by a singlememory portion. Further, the memory storage performed by one distinctmemory portion as described above may be performed by two memoryportions.

Further, various technologies may be used to provide communicationbetween the various processors and/or memories, as well as to allow theprocessors and/or the memories of the invention to communicate with anyother entity; i.e., so as to obtain further instructions or to accessand use remote memory stores, for example. Such technologies used toprovide such communication might include a network, the Internet,Intranet, Extranet, LAN, an Ethernet, or any client server system thatprovides communication, for example. Such communications technologiesmay use any suitable protocol such as TCP/IP, UDP, or OSI, for example.

As described above, a set of instructions is used in the processing ofthe invention. The set of instructions may be in the form of a programor software. The software may be in the form of system software orapplication software, for example. The software might also be in theform of a collection of separate programs, a program module within alarger program, or a portion of a program module, for example. Thesoftware used might also include modular programming in the form ofobject-oriented programming. The software tells the processing machinewhat to do with the data being processed.

Further, it is appreciated that the instructions or set of instructionsused in the implementation and operation of the invention may be in asuitable form such that the processing machine may read theinstructions. For example, the instructions that form a program may bein the form of a suitable programming language, which is converted tomachine language or object code to allow the processor or processors toread the instructions. That is, written lines of programming code orsource code, in a particular programming language, are converted tomachine language using a compiler, assembler or interpreter. The machinelanguage is binary coded machine instructions that are specific to aparticular type of processing machine, i.e., to a particular type ofcomputer, for example. The computer understands the machine language.

Any suitable programming language may be used in accordance with thevarious embodiments of the invention. Illustratively, the programminglanguage used may include assembly language, Ada, APL, Basic, C, C++,COBOL, dBase, Forth, Fortran, Java, Modula-2, Pascal, Prolog, REXX,Visual Basic, Java, JavaScript, Pert, Python, Common List and/or Scheme,for example. Further, it is not necessary that a single type ofinstructions or single programming language be utilized in conjunctionwith the operation of the system and method of the invention. Rather,any number of different programming languages may be utilized as isnecessary or desirable.

Also, the instructions and/or data used in the practice of the inventionmay utilize any compression or encryption technique or algorithm, as maybe desired. An encryption module might be used to encrypt data,including authentication and digital signatures in order to protect theintegrity of data and/or users. Further, files or other data may bedecrypted using a suitable decryption module, for example.

As described above, the invention may illustratively be embodied in theform of a processing machine, including a computer or computer system,for example, that includes at least one memory. It is to be appreciatedthat the set of instructions, i.e., the software for example thatenables the computer operating system to perform the operationsdescribed above may be contained on any of a wide variety of media ormedium, as desired. Further, the data that is processed by the set ofinstructions might also be contained on any of a wide variety of mediaor medium. That is, the particular medium, i.e., the memory in theprocessing machine, utilized to hold the set of instructions and/or thedata used in the invention may take on any of a variety of physicalforms or transmissions, for example. Illustratively, the medium may bein the form of paper, paper transparencies, a compact disk, a DVD, anintegrated circuit, a hard disk, a floppy disk, an optical disk, amagnetic tape, a RAM, a ROM, a PROM, an EPROM, a wire, a cable, a fiber,communications channel, a satellite transmissions or other remotetransmission, as well as any other medium or source of data that may beread by the processors of the invention.

Further, the memory or memories used in the processing machine thatimplements the invention may be in any of a wide variety of forms toallow the memory to hold instructions, data, or other information, as isdesired. Thus, the memory might be in the form of a database to holddata. The database might use any desired arrangement of files such as aflat file arrangement or a relational database arrangement, for example.

In the system and method of the invention, a variety of “userinterfaces” may be utilized to allow a user to interface with theprocessing machine or machines that are used to implement the invention.As used herein, a user interface includes any hardware, software, orcombination of hardware and software used by the processing machine thatallows a user to interact with the processing machine. A user interfacemay be in the form of a dialogue screen for example. A user interfacemay also include any of a mouse, touch screen, keyboard, voice reader,voice recognizer, dialogue screen, menu box, list, checkbox, toggleswitch, a pushbutton or any other device that allows a user to receiveinformation regarding the operation of the processing machine as itprocesses a set of instructions and/or provide the processing machinewith information. Accordingly, the user interface is any device thatprovides communication between a user and a processing machine. Theinformation provided by the user to the processing machine through theuser interface may be in the form of a command, a selection of data, orsome other input, for example.

As discussed above, a user interface is utilized by the processingmachine that performs a set of instructions such that the processingmachine processes data for a user. The user interface is typically usedby the processing machine for interacting with a user either to conveyinformation or receive information from the user. However, it should beappreciated that in accordance with some embodiments of the system andmethod of the invention, it is not necessary that a human user actuallyinteract with a user interface used by the processing machine of theinvention. Rather, it is also contemplated that the user interface ofthe invention might interact, i.e., convey and receive information, withanother processing machine, rather than a human user. Accordingly, theother processing machine might be characterized as a user. Further, itis contemplated that a user interface utilized in the system and methodof the invention may interact partially with another processing machineor processing machines, while also interacting partially with a humanuser.

It will be readily understood by those persons skilled in the art thatthe present invention is susceptible to broad utility and application.Many embodiments and adaptations of the present invention other thanthose herein described, as well as many variations, modifications andequivalent arrangements, will be apparent from or reasonably suggestedby the present invention and foregoing description thereof, withoutdeparting from the substance or scope of the invention.

Accordingly, while the present invention has been described here indetail in relation to its exemplary embodiments, it is to be understoodthat this disclosure is only illustrative and exemplary of the disclosedsubject matter and is made to provide an enabling disclosure of thesubject matter. Accordingly, the foregoing disclosure is not intended tobe construed or to limit the disclosed subject matter or otherwise toexclude any other such embodiments, adaptations, variations,modifications and equivalent arrangements.

While particular embodiments of the disclosed subject matter have beenillustrated and described in detail herein, it should be understood thatvarious changes and modifications might be made to the disclosed subjectmatter without departing from the scope and intent of the disclosedsubject matter.

From the foregoing it will be seen that the disclosed subject matter iswell adapted to attain the ends and objects set forth above, togetherwith other advantages. It will be understood that certain features andsub-combinations are of utility and may be employed without reference toother features and sub-combinations. This is contemplated and within thescope of the disclosed subject matter.

TABLE 1 Materials Candidate Property Material Material PerformanceIndustrial Research Selection Synthesis Testing SynthesisCommercialization Phases 1 2 3 4 5 Sources of value Curate digitalPredict candidate Fabricate desired Characterize Devise industrialdatabase of material material candidate synthesized material process torepeat: properties (i.e. compositions and materials in lab** properties(in-situ and synthesis on large physical, spectral, structures based onin application) and manufacturing and transport select propertiescompare vs desired scales properties, energy properties levels etc.)Challenges Limited number of Candidate material Slow and Instrumentaltesting in Synthesis steps materials established selection uncertainuncertain, difficult simplified conditions exhibit failures byexperimentation and based on trial to synthesize large not reflective ofduring scale up or computation and error, databases number of industryapplications and must be limited in nu, of compositionally optimized formaterials and varying samples industrial scale properties with cost andefficiency Value Unlock Generate a large Select more accurate ComputerMore predictive Devise optimal Efficiency in repository of candidatematerials simulation simulation based on synthetic routes Design, fastercomputationally from database of eliminates testing both in in-situ thatwork for revenue realization, calculated materials, simulated propertiesuncertainty and and in-operando industrial new materials, leanproperties and inefficiencies reactors manufacturing energy levels

TABLE 2 Early NISQ Late NISQ Broad Quantum Advantage Full-scale FaultTolerance Commercial (2-5 yrs) (5-10 yrs) (10+ yrs) (20+ yrs) SourcesPhase 1 2 3 4 of value Error Mitigation: Recursive Error Correction(large Modular Architecture (code independent numbers of qubitsentangled) circuitry) Technical # of physical qubits, ft of logicalqubits, qubit lifetime, gate fidelity, gate operation time,connectivity, challenges scalability, maturity Limited coherence Limiteduse of Error correction, quantum Dynamic simulations, quantum phaseEfficiency in time, heuristic Quantum auto- simulations, distributedestimation, Quantum Machine Learning Design, error correction encoder,architecture accelerated techniques, as well as develop., limitedsimulation library content faster enhancements & access, revenue Valuerealization, Unlock lower cost, More efficient Improved Ability to runRecursive error Portable and Enhanced in- Algorithms and new classicalsystem Functional correction, distributed silco product that providehigher simulations designs Quantum Photon quantum testing thatnon-intuitive quality based on the Simulations, based computing accountscorrelations to products merits of Error distributed processors for useimproving enhanced DFT; mitigation architecture environment processesthat enhanced VQE increase yields

TABLE 3 Single functionality chemical Commercial ingredient Formulationor Compound Application Development Manufacturing Sources of Phase 1 2 34 5 6 7 value Synthesis Lab tests to Mixing, Lab test to check ProcessLab test to Manufacturing check desired Blending, desired propertiesEngineering check desired properties Compounding products ChallengesVariability, lots of approximations, Time consuming, overarchingly Lackof integrated simulation-optimization- time consuming, not verytargeted, empirical, lack of simulation- artificial intelligence toolsto limited tools, highly empirical optimization-artificial intelligenceoptimize reactor set-up and tune tools for formulation developmentchemical process conditions of mixtures or complex assemblies Choosingthe best Physical Procedures & Numerous trials, Synthesis at Producttest Lack of ideal Efficiency in small molecule, attempts toPermutations ex. physical scale involves limitations, Mixing & Design,polymers & measure Compounding, measurement of pitfalls of numeroustrials, Blending, accelerated composites properties ex. formulas,properties, lack tremendous constrained in- Formulation at develop.,candidates Tribology, OLED of automation ex. inefficienciessilco/in-situ & scale, faster Quantum stacks toxicity, stack qualitytesting, ex. OLED revenue efficiency Life-time ex. pixel TV massrealization, reliability production lower cost, and new Value higherUnlock quality More efficient in- Designs of new Design of AutomatedApplication of Enhanced in- Algorithms that products silco functionalimproved Simulations that break through silco product provide non-experimentation molecules that products based reflect in-situ catalyststhat testing that intuitive based on the rely on the on improvedactivity reduce accounts for use correlations to merits of accuratecomplex conditions byproducts environment improving enhanced DFTpredictions of techniques & and energy processes that micro & macroprocesses consumption increase yields properties

TABLE 4 Regulatory Target Assay Clinical Submission & Commercial TargetID Validation Development Screening Optimization Preclinical TrialsReview Sources of Phases 1 2 3 4 5 6 7 8 value ID disease Target Developtests ID hit Optimize Study Test drug in Submit for drivers Validationto measure compounds hits, select metabolism, humans for approval targetdrug toxicology, efficacy, impact candidate etc. safety and dosingChallenges (Variable) Time And Cost ~4.5 yrs at (~1 yr (~6 yrs. @~$700M) ~$200M) ~$1.2B- $1.7B) Weak Experimental Unreliability Lack ofInability to Low >90% failure Uncertainty and signal limitations ofexhaustive optimize predictive rate, high launch delays in large testingsearch some value cost data sets hits Value Unlock Better AlgorithmsVirtual Virtual Significantly Algorithms Algorithms Rapid analysis ofEfficiency in algorithms that reflect screening of screening of improvedthat better that simulate clinical trials and Design, faster higherhuman system large virtual large virtual drug predict the drug patientother data sources revenue computing libraries libraries design humaninteractions realization, power system lower cost, new and moreeffective drugs approved

TABLE 5 Target Asset Adj. Due Detection of Profit Portfolio Assets toMrkt Opportunity Extraction Risk Assessment Return Commercial Phases 1 23 4 5 6 Sources of value ID Assets to How should What are the How totake How to estimate Return of a be included composition opportunitiesprofit by risk portfolio in portfolio change with with the differenttrading with the market assets in the them market Challenges Requirescolossal computation power to accurately describe the market system,becomes worse as data gathered related to market increases! DynamicPortfolio Selection Machine Learning (Neural Monte Carlo Simulation(More complex problems calls networks, deep learning) requires many runsto provide an for more elaborate methods Network training time too long,accurate estimation of the other than linear programs) running newmodels with large expected return and distribution. amounts of databecomes cost Moreover, shows less prediction prohibitive accuracy forshort periods Value Unlock Develop Computer Model trained to identifypatterns Reduce the number of samples Better products, more NiSQsimulations in the data & predict the behavior while increasing theaccuracy in a products to a wiser Algorithm (annealing of new datapoints quickly and shorter amount of time consumer base, Librariessimulations) cost effectively revenue growth, Big Big data Random Accessgreater profits, better Memory managed operations

What is claimed is:
 1. A Quantum Mechanics Instruction Production (QMIP)system comprising: a QMIP processing core including one or moreprocessors and one or more QMIP core process modules; an input modulecoupled to the QMIP processing core; an output module coupled to theQMIP processing core; a database library module coupled to the QMIPprocessing core; a material printer coupled to the QMIP processing core;a sample analyzer coupled to the QMIP processing core; and a test benchcoupled to the QMIP processing core, wherein the one or more processorsare coupled to a computer-readable medium having stored thereon softwareinstructions that, when executed by the one or more processors, causethe one or more processors to perform operations including: obtaininginput requirements via the input module; searching the database librarymodule for an existing material matching the input requirements; when anexisting material meets the input requirements, outputting existingmaterial information via the output module; when an existing materialdoes not meet the input requirements, determining one or more newcandidate materials using the one or more QMIP core process modules by:performing first Grand Free Energy computations; computing NosanowFermion Wave Equation results; performing second Grand Free Energycomputations; simulating the one or more new candidate materials basedon the Nosanow Fermion Wave Equation; confirming that the one or morenew candidate materials based on the Nosanow Fermion Wave Equation meetthe input requirements; and outputting information on confirmed ones ofthe one or more new candidate materials determined by the QMIPprocessing core via the output module.
 2. The QMIP system of claim 1,wherein the one or more QMIP core process modules include: a Grand FreeEnergy module; and a Nosanow Fermion Wave module, wherein the firstGrand Free Energy computation and the second Grand Free Energycomputation are performed by the Grand Free Energy module, and whereinthe Nosanow Fermion Wave Equation computations are performed by theNosanow Fermion Wave module.
 3. The QMIP system of claim 2, wherein theone or more QMIP core process modules further include: a simulationmodule; an artificial intelligence module; a chemical bench module; anda metrology and interferometry module, wherein the simulating of the oneor more new candidate materials is performed by one or more of thesimulation module and the chemical bench module, and wherein theconfirming of the one or more new candidate materials is performed byone or more of the chemical bench module, the metrology andinterferometry module, a test bench module, and a sample analyzermodule.
 4. The QMIP system of claim 3, further comprising: a license andauthorization security module; and a tradeoff module.
 5. The QMIP systemof claim 4, wherein the input requirements include one or more ofchemical, electrical, thermal, and electromagnetic properties.
 6. TheQMIP system of claim 5, wherein when the one or more new candidatematerials includes two or more candidate materials, performing atradeoff analysis using the tradeoff module and the artificialintelligence module.
 7. The QMIP system of claim 6, wherein the inputrequirements include specification of a transmon or Josephson junction.8. The QMIP system of claim 7, wherein performing the first Grand FreeEnergy computations includes: calculating Grand Free Energy for acandidate new material; determining a Grand Partition Function Tracebased on the Grand free Energy calculation; computing Hamiltonians; andapplying a variational theorem to determine an energy upper bound forthe candidate new material.
 9. The QMIP system of claim 8, whereincomputing the Nosanow Fermion Wave Equation results includes: defining aNosanow wave system; determining a commutator for free particles;determining a commutator for interaction particles; computing a timeindependent equation for the Nosanow Fermion Wave Equation; and applyingelectromagnetic field to the time independent equation for the NosanowFermion Wave Equation.
 10. The QMIP system of claim 9, whereinperforming the second Grand Free Energy computations includes:determining an Eigenvalue spectrum solution and substituting into theGrand Partition Function Trace; applying the variational theorem todefine solutions provided by the Nosanow Fermion Wave Equation; anddefining phase transitions from solutions of Nosanow Fermion Wavefunctions to determine Tc.
 11. The QMIP system of claim 1, wherein thesoftware instructions further include instructions that, when executedby the one or more processors, cause the one or more processors toperform further operations including: determining set-up parameters forconfiguring a quantum computer having one or more qubits based on aselected one of the candidate new materials; providing the set-upparameters to a quantum computer controller; determining one or moreoperational control parameters for the quantum computer based on theselected one of the candidate new materials; and providing theoperational control parameters to the quantum computer controller. 12.The QMIP system of claim 11, wherein the quantum computer controller isa classical computing device having one or more processors and whereinthe quantum computer controller is integrated with the QMIP system. 13.The QMIP system of claim 12, wherein the software instructions furtherinclude instructions that, when executed by the one or more processors,cause the one or more processors to perform further operationsincluding: sensing one or more of temperature, pressure, and energyapplied to the one or more qubits; and adjusting one or more of thetemperature, pressure, and energy applied to the one or more qubits tomaintain coherency of the one or more qubits below Tc.
 14. The QMIPsystem of claim 13, wherein the one or more qubits includes a transmon.15. The QMIP system of claim 13, wherein the one or more qubits includesa Josephson junction.
 16. The QMIP system of claim 13, wherein theenergy includes one of microwave energy or laser energy.
 17. The QMIPsystem of claim 13, wherein the one or more qubits includes two or morequbits, and wherein the software instructions further includeinstructions that, when executed by the one or more processors, causethe one or more processors to perform further operations including:generating Nosanow Fermion Wave Equation functions to couple the two ormore qubits; and generating control signals for the quantum computercontroller to maintain coherency of the coupled two or more qubits. 18.The QMIP system of claim 17, wherein the one or more qubits includemulti-dimensional Nosanow qubits, wherein dimensions of themulti-dimensional Nosanow qubits includes a spin dimension, and whereindetermining the one or more operational control parameters for thequantum computer is based on Nosanow Fermion Wave Equation functions andincludes utilizing the spin dimension of the multi-dimensional Nosanowqubits to generate control parameters and control signals from thequantum computer controller to maintain coherency of themulti-dimensional Nosanow qubits.
 19. The QMIP system of claim 18,wherein the spin dimension of the multi-dimensional Nosanow qubits isused to generate control signals to keep the selected one of thecandidate new materials in a superconductor zone and to extend coherencyof the multi-dimensional Nosanow qubits.
 20. A Quantum MechanicsInstruction Production (QMIP) system comprising: a QMIP processing coreincluding one or more processors and one or more QMIP core processmodules; an input module coupled to the QMIP processing core; an outputmodule coupled to the QMIP processing core; a database library modulecoupled to the QMIP processing core; a material printer coupled to theQMIP processing core; a sample analyzer coupled to the QMIP processingcore; and a test bench coupled to the QMIP processing core, wherein theone or more processors are coupled to a computer-readable medium havingstored thereon software instructions that, when executed by the one ormore processors, cause the one or more processors to perform operationsincluding: obtaining input requirements via the input module; searchingthe database library module for an existing material matching the inputrequirements; when an existing material meets the input requirements,outputting existing material information via the output module; when anexisting material does not meet the input requirements, determining oneor more new candidate materials using the one or more QMIP core processmodules by: performing first Grand Free Energy computations; computingNosanow Fermion Wave Equation results; performing second Grand FreeEnergy computations; simulating the one or more new candidate materialsbased on the Nosanow Fermion Wave Equation; confirming that the one ormore new candidate materials based on the Nosanow Fermion Wave Equationmeet the input requirements; and outputting information on confirmedones of the one or more new candidate materials determined by the QMIPprocessing core via the output module, wherein the one or more QMIP coreprocess modules include: a Grand Free Energy module; and a NosanowFermion Wave module, wherein the first Grand Free Energy computation andthe second Grand Free Energy computation are performed by the Grand FreeEnergy module, and wherein the Nosanow Fermion Wave Equationcomputations are performed by the Nosanow Fermion Wave module, whereinthe one or more QMIP core process modules further include: a simulationmodule; an artificial intelligence module; a chemical bench module; anda metrology and interferometry module, wherein the simulating of the oneor more new candidate materials is performed by one or more of thesimulation module and the chemical bench module, wherein the confirmingof the one or more new candidate materials is performed by one or moreof the chemical bench module, the metrology and interferometry module, atest bench module, and a sample analyzer module, and wherein thesoftware instructions further include instructions that, when executedby the one or more processors, cause the one or more processors toperform further operations including: determining set-up parameters forconfiguring a quantum computer having one or more qubits based on aselected one of the candidate new materials; providing the set-upparameters to a quantum computer controller; determining one or moreoperational control parameters for the quantum computer based on theselected one of the candidate new materials; and providing theoperational control parameters to the quantum computer controller.